CN102611897A - Method and system for carrying out vision perception high-fidelity transformation on color digital image - Google Patents

Abstract

The invention relates to the field of digital image processing, in particular to a method and system for visually perceiving high-fidelity transformation of color digital images. The method comprises the steps of: (1) obtaining the L, C and h of the color digital image pixel color R, G and B values in CIELAB space; (2) merging the pixel color into the hue phase plane and brightness sequence; (3) completing the pixel The expansion of the color saturation C; (4) calculating the image pixel color parameters and the saturation C _LN values obtained after the expansion in step (3) as R, G and B values. According to the FECr algorithm of the present invention, under the condition that the image color space boundary composed of red, green and blue primary colors does not change, the visually sensitive color in the calculation image is increased to increase relatively more visible light color visual perception characteristics, and the calculation visual perception is relatively large. The less sensitive color increases less or remains unchanged. This non-linear adjustment method can make the viewed image relatively significantly closer to the effect of viewing the actual scene in the visible light color space.

Description

Method and system for visual perception and high-fidelity transformation of color digital images

technical field

The invention relates to the field of digital image processing, in particular to a method and system for visually perceiving high-fidelity transformation of color digital images.

Background technique

The use of digital quantities of the primary colors red, green and blue has been used in electronic devices to capture or display color images for decades. Although the three primary colors are selected to make the display image as faithful as possible to the real scene, due to material, technology and cost constraints, there is still a big gap between viewing the color image displayed by the three primary colors and viewing the real scene so far. These include that the color is not full enough, the light is not bright enough, and so on.

The color management system represented by the ICC (International Color Consortium International Color Consortium) specification typically uses the gamma (gamma factor) index to adjust the digital quantities of the three primary colors of red, green and blue to achieve the purpose of image enhancement. Users found that although the gamma index can make the color of the image look fuller, the sense of light will be worse, and the color tone will also deviate. Anyone using the chromaticity method recommended by CIE (International Commission on Lumination) can quantitatively calculate the number of changes in color visual perception components before and after using the gamma index, and prove that the results are consistent with the changes actually seen above.

After more than 20 years of development, the CAM (Color Appearance Model) recommended by CIE has become an international recommended standard and plays an important role in the field of color reproduction. However, whether it is the CIECAM97s or CIECAM02 model, the main research and prediction is the change of external conditions, such as the quantitative impact of the reference white value and brightness value changes on the color observation effect, the model does not include the space and time of human visual perception characteristics, and does not include the spatial and temporal domain characteristics of the image. The current application of CAM to digital image processing regards each pixel as an independent color, so the reproduction and reproduction of digital images guided by it cannot meet the high-quality needs of visual perception authenticity. Recently, related industries have proposed to build iCAM (image Color Appearance Model image color appearance model), which requires the model to be able to deal with visual perception and the spatial and temporal characteristics of color images. At present, the iCAM model is still in the initial stage of research, and CIE has not yet discussed it in detail and fully recommended it.

The quantitative description of the spatial characteristics of human visual perception of visible light color has not been fully completed so far, mainly due to the incompleteness of the basic data required for the quantification of visual perception of color. CIE recommended the CIE1931RGB system in 1931. The standard chromaticity observer spectral tristimulus value described by it quantified human color visual perception for the first time in history, and thus laid the foundation for modern colorimetry. However, some inherent characteristics of this system failed to be perfected in the subsequent theoretical research and technical application of colorimetry. In the CIE1931RGB system, the spectral tristimulus value represents the matching quantity of various monochromatic lights in the equal energy spectrum using the red, green and blue primary colors. The coordinates of the spectral trajectory calculated by its matching function are shown in Figure 1. The horseshoe-shaped curve in the figure represents the spectral locus, and the colors in the area covered by the curve and the lower straight line have been mistaken for a long time to include all the colors that humans can see. The area inside the triangle represents the color range where all positive values of the red, green and blue primary colors match. Colors with negative values of the primary colors should be in the area outside the triangle and inside the curve. The essence of color negative is to use the red, green and blue primary colors to match the difference between spectral colors. The physical meaning of a negative color is the opposite color of the non-negative color, that is, the color that is added to the non-negative color to obtain an achromatic color is its opposite color. The fact that there is a negative value in the matching function shows that to match the spectral colors accurately, the above-mentioned opposite colors need to be added to the three primary colors of red, green and blue.

Because the colors displayed at the same time with double monochrome and above multi-spectral colors are normal for human visible colors, it is speculated from the above that the CIE1931RGB matching function does not include the combination of red and blue primary colors with positive values and the visible light colors with negative values of green primary colors. The colors in some hue intervals include blue-violet, purple and purple-red, which account for nearly one-third of the number of colors perceived by human vision, and a small number of colors should have chromaticity coordinates below the straight line in Figure 1 above. If the research and application of high-fidelity color simulation reproduction and reproduction are carried out with the visual perception characteristics of visible light as the goal, this part of the matching function should not be missing. The color matching function, which is the cornerstone of modern chromaticity, cannot express the complete boundary of the spatial distribution of visible light color visual perception space in CIELAB visual perception uniform space, so that the quantitative description of the visible light spatial characteristics of human visual perception has not been fully completed so far.

Contents of the invention

The purpose of the invention is to provide a method for performing high-fidelity visual perception transformation for color digital images. According to the technical solution of the present invention, by providing a method for calculating the visual perception space of visible light color and the equivalent of related components, the optimization expansion and mapping calculation of the visual perception component ratio of color digital images to the visual perception space characteristics of visible light color are realized, and the high-resolution image is completed. True visual perception transformation.

Another object of the present invention is to provide a system for high-fidelity visual perception transformation of color digital images.

The method and system of the present invention belong to "Epic Color ^TM ", (FirstEposColor ^TM ), referred to as "Poetry Color ^TM ", (FECr ^TM ), mainly to adjust the visual perception of each pixel color in order to significantly enhance the high-fidelity perception of images. Perceptual saturation value, the adjustment amount depends on the saturation value of the brightness sequence on the equivalent color boundary of the visible light color visual perception space hue phase plane corresponding to the color and the saturation value of the color itself. As a result, the color of each pixel of the image increases the visual perception saturation while maintaining the same visual perception hue and brightness, and the increased saturation range is more conducive to high-fidelity enhancement of the visual perception effect of the image.

A typical color digital image is a two-dimensional projection of the real scene in the visible light color space on the imaging plane. Therefore, the ideal effect of image reproduction and reproduction should be as close as possible to the perception of the real scene, which is called high-fidelity reproduction and reproduction. Therefore, the algorithm (FECr) according to the invention includes a quantitative representation of the visual perception in the visible light color space. According to the method of the present invention, the visual perception saturation of the inherent color of the image is extended and mapped relative to the above-mentioned visible light spatial characteristics, and the saturation value of each pixel is calculated with the goal of obtaining the overall relatively largest visible light spatial characteristics of the image.

The color visual perception space of typical electronic devices and color images is much smaller than the visual color perception space of visible light. Therefore, if the image color saturation is adjusted directly with the boundary of the visible light color space as the target, in some hue planes, the pixel color saturation will be caused. The difference in increments is very large, resulting in image degradation. Therefore, according to the FECr algorithm of the present invention, on the basis of a complete description of the visible light color visual perception space, taking into account the actual ability of the device and the nonlinear expansion of the image color visual perception space saturation, the optimized visible light color visual perception space hue phase plane color is calculated Equivalent boundaries, and corrected by psychophysical methods to obtain statistical data of applied effects. Therefore, the method according to the invention is an effective method for obtaining high-fidelity reproduction and reproduction of color digital images.

The method and system of the present invention can be used for natural scene imaging such as photography, videography, film, television, video games, or any image synthesized by red, green and blue primary colors generated by a computer, and related imaging equipment.

According to the method of the present invention, carrying out visual perception high-fidelity transformation to the color digital image comprises the following steps:

(1) Obtain the L, C and h values of the color digital image pixel color R, G and B values in the CIELAB space, wherein, h is the hue angle, L is the brightness, and C is the saturation;

(2) The pixel color is merged into the hue plane and brightness sequence,

Merge into the corresponding reference hue phase plane with the hue angle h value, merge into the corresponding brightness sequence with the brightness L value;

(3) Complete the expansion of pixel color saturation C,

(3-1) Determine pixel color saturation mapping inflection point C _gL :

C _gL ＝C _maxL1 ×X _S1

Among them, C _maxL1 is the maximum saturation value of the brightness sequence in which the pixel color is merged into the hue phase plane in the device's color visual perception space, and X _S1 is the set proportional coefficient, with a typical value range of 0.65-0.95;

(3-2) Calculate the saturation expansion ratio BI _L of the brightness sequence:

BI _L =C _maxLd /C _maxL1

Among them, C _maxLd is the saturation value of the same brightness sequence on the color equivalent boundary of the pixel color with the same hue phase plane in the visible light color visual perception space;

(3-3) Saturation expansion ratio BI _L characteristic control calculation, including:

(3-3-1) Set the saturation expansion ratio control coefficient BI _Kx , the value range is 0.00-1,

BI _LY = BI _L × BI _Kx

Among them, BI _Kx allows multiple settings to be used for specified conditions, BI _LY is the saturation expansion ratio after regulation,

(3-3-2) Set the paired boundaries _HDx and H _Gx of the hue plane interval, the value range is 0°-359°, _HDx and H _Gx allow multiple pairs to be used for the specified conditions respectively,

Color h≥H _Dx and h≤H _Gx , BI _LY =BI _L ×BI _K2

For other colors, BI _LY = BI _L × BI _K1

The value range of the hue phase plane transition area inside the boundary is set to 0-20,

(3-3-3) Set the saturation ratio threshold C _Gx , the value range is 0.00-1, C _Gx allows multiple settings for specified conditions,

C _Gx value below the color, BI _LY = BI _L × BI _K2

C _Gx value above the color, BI _LY = BI _L × BI _K1

C _Gx value high-end side transition area setting value range 0.00-0.2,

(3-4) Calculate the pixel color saturation C _L after expansion C _LN :

C _LN =C _L ×BI _LY ,

If C _LN is greater than C _gL , calculate its mapping:

C _LN =C _gL +(C _LN -C _gL )/(C _maxLd -C _gL )×(C _maxL1 -C _gL );

(4) Calculate the image pixel color brightness L, hue angle h, and saturation C _LN values obtained after step (3) expansion as standard R, G, and B values.

According to the method of the present invention, step (1) is to obtain the L, C and h values of the color digital image pixel colors R, G and B values in CIELAB space, wherein, h is the hue angle, L is brightness, C is saturation, It can be realized by existing known methods in the art. Preferably, according to the specific embodiment of the present invention, the step of invoking the device visual perception color space hue phase plane color boundary calculation module is first performed to obtain the necessary 3×3 matrix coefficients.

According to the present invention, the system for visual perception and high-fidelity transformation of color digital images includes:

(1) The color visual perception space hue phase plane color boundary calculation module of the equipment displaying color digital images, including:

(1-1) The red, green and blue primary color values of the equipment color space are transformed into CIELAB space L, C and h value calculation units,

(1-2) Device color visual perception space hue phase plane color boundary extraction unit,

The device color is rounded and merged with the hue h value into the corresponding reference hue phase plane, rounded and merged with the L value into the corresponding brightness sequence, and the maximum color saturation value C _maxL1 of each brightness sequence of the hue phase plane is extracted as the color boundary calculation of the hue phase plane base value,

(1-3) Hue phase plane color boundary C _maxL1 smoothing unit,

Select the color boundary C _maxL1 corresponding to the brightness sequence interval from the brightness L _Cmaxh1 of the maximum saturation value C _maxh1 in the hue phase plane to the lowest brightness L=0, calculate the smooth boundary with a standard linear interpolation algorithm, and make up for the non-smooth decrease or decrease of C _maxL1 Fill in the missing. The calculated color boundary and the color boundary corresponding to the luminance sequence interval from luminance L _Cmaxh1 to L=100 represent the applied color boundary C _maxL1 of the hue phase plane;

(2) C _maxLd calculation module of the color equivalent boundary C maxLd of the color phase plane of the visible light color visual perception space;

(3) Forward conversion of color digital image pixel color mode and merging of hue phase plane and brightness sequence module, including:

(3-1) The calculation unit that converts color digital image pixel color RGB value into L, C and h value of CIELAB space, wherein, h is hue angle, L is brightness, C is saturation,

(3-2) pixel color hue phase plane and brightness sequence merging unit,

Divide the image color space into 360 reference color phase planes, round off the hue h value and enter the corresponding reference color phase plane, divide the brightness L range in the hue phase plane into 101 reference sequences, round up and merge into the corresponding brightness according to the brightness L value sequence;

(4) image pixel color saturation value expansion module, including:

(4-1) Pixel color saturation expansion ratio BI _L calculation unit:

BI _L =C _maxLd /C _maxL1

Among them, C _maxLd is the saturation value of the brightness sequence of the pixel on the color equivalent boundary of the visible light hue phase plane, and C _maxL1 is the saturation value of the brightness sequence of the pixel on the color boundary of the device hue phase plane,

(4-2) Saturation expansion ratio BI _L characteristic regulation calculation unit, including:

(4-2-1) Set the saturation expansion ratio control coefficient BI _Kx , the value range is 0.00-1,

BI _LY = BI _L × BI _Kx

Among them, BI _Kx allows multiple settings, which are used to specify conditions respectively, and BI _LY is the saturation expansion ratio after regulation,

(4-2-2) Set the paired boundaries _HDx and H _Gx of the hue phase interval, the value range is 0°-359°, _HDx and H _Gx allow multiple pairs to be used for the specified conditions:

Color h≥H _Dx and h≤H _Gx , BI _LY =BI _L ×BI _K2

For other colors, BI _LY = BI _L × BI _K1

The value range of the hue phase plane transition area inside the boundary is set to 0-20,

(4-2-3) Set the saturation ratio threshold C _Gx , the value range is 0.00-1, and C _Gx allows multiple settings to be used for the specified conditions:

C _Gx value below the color, BI _LY = BI _L × BI _K2

C _Gx value above the color, BI _LY = BI _L × BI _K1

C _Gx value high-end side transition area setting value range 0.00-0.2,

(4-3) Saturation mapping inflection point C _gL calculation unit:

C _gL ＝C _maxL1 ×X _S1

Set the scale factor X _S1 value range 0.65-0.95,

(4-4) The pixel color saturation C _L is extended to the C _LN calculation unit:

C _LN =C _L ×BI _LY ,

Judgment, if C _LN is greater than C _gL then calculate the mapping:

C _LN =C _gL +(C _LN -C _gL )/(C _maxLd -C _gL )×(C _maxL1 -C _gL );

(5) Image pixel color mode inverse transformation and normalization module:

The calculation includes calculating the L, h of the image pixel color and the saturation C _LN value obtained by the expansion of the module (4) as normative R, G and B values.

According to a preferred embodiment of the present invention, the system further includes a module for calculating the maximum color saturation of the visible light color visual perception space hue phase plane.

As a preferred technical solution of the present invention, the device visual perception color space hue phase plane color boundary database is first calculated by the device visual perception color space hue phase plane color boundary calculation module in the system of the present invention, and the calculation includes:

(1-1) The red, green and blue primary color values of the device color space are transformed into the calculation of CIELAB space L, C and h values, and the red, green and blue primary color values of the device color space are transformed into CIELAB space L, C and h The value computation unit executes:

Using the equipment's nominal white field and corresponding parameters of red, green and blue primary colors, and applying the standard algorithm recommended by CIE, convert all the colors synthesized by the equipment's red, green and blue primary colors into CIEXYZ tristimulus values and CIELAB space L, C and h values, including:

The luminance value of the maximum saturation of the RGB three primary colors is calculated by applying the nominal sRGB space RGB three primary color chromaticity parameters and the D65 white point parameter:

Y _{r, max} = 0.2126 Y _{g, max} = 0.7152 Y _{b, max} = 0.0722

Apply the above calculation to get the 3×3 matrix coefficients:

$\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 0.4124</span>}& \mathrm{<span\; class="notranslate">\; 0.3576</span>}& \mathrm{<span\; class="notranslate">\; 0.1805</span>}\\ \mathrm{<span\; class="notranslate">\; 0.2126</span>}& \mathrm{<span\; class="notranslate">\; 0.7152</span>}& \mathrm{<span\; class="notranslate">\; 0.0722</span>}\\ \mathrm{<span\; class="notranslate">\; 0.0193</span>}& \mathrm{<span\; class="notranslate">\; 0.1192</span>}& \mathrm{<span\; class="notranslate">\; 0.9505</span>}\end{array}\right]$

For non-standard equipment, it is necessary to calculate the CIEXYZ tristimulus value at the white point and the maximum saturation of the three primary colors of red, green and blue:

Using a standard spectrophotometer, measure the white field tristimulus values X _w ', Y _w ', and Z _w ' of the equipment according to conventional specifications, and calculate the white field normalization coefficient K ₁ :

K ₁ =100/Y _w '

Calculate the CIEXYZ tristimulus value for the white field of the device:

X _w ＝X _w '×K ₁ Y _w ＝Y _w '×K ₁ Z _w ＝Z _w '×K ₁

Using a standard spectrophotometer, measure the tristimulus values under the condition of maximum saturation of red, green and blue primary colors, X _r ', Y _r ' and Z _r ', X _g ', Y _g ' and Z _g respectively according to conventional specifications ', _Xb ', _Yb ' and _Zb '. Calculate the CIEXYZ tristimulus values of the three primary colors separately:

X _r,max =X _r '×K ₁ , Y _r,max =Y _r '×K ₁ , Z _r,max =Z _r '×K ₁

X _g,max =X _g '×K ₁ , Y _g,max =Y _g '×K ₁ , Z _g,max =Z _g '×K ₁

X _b,max =X _b '×K ₁ , Y _b,max =Y _b '×K ₁ , Z _b,max =Z _b '×K ₁

Use the CIEXYZ tristimulus values of the three primary colors calculated above to replace the 3×3 matrix coefficients in the above-mentioned standard method, and use the calculated white field CIEXYZ tristimulus values to replace the nominal white field CIEXYZ tristimulus values of the above-mentioned equipment, and calculate CIELAB space L, C and h values.

(1-2) The calculation of the color boundary of the hue phase plane in the visual perception color space of the device is performed by the color boundary extraction unit of the hue phase plane in the visual perception space of the device:

Integers from 0 to 359 represent the reference hue plane, and the hue h value is rounded and merged into the corresponding reference hue phase plane, and an integer from 0 to 100 is used to represent the reference brightness sequence, and the L value is rounded and merged into the corresponding brightness sequence to extract the color phase The maximum color saturation value C _maxL1 in each brightness sequence of the plane is used as the basis for calculating the color boundary of the hue phase plane;

(1-3) The smoothing calculation of the color boundary of the hue phase plane is performed by the color boundary C _maxL1 smoothing unit of the hue phase plane:

Extract the color boundary corresponding to the luminance sequence interval from the maximum saturation value C _maxh1 in the hue phase plane _to the minimum luminance L=0, and calculate the smooth boundary with a standard linear interpolation algorithm to compensate for the maximum saturation C of the original luminance sequence _maxL1 non-smooth decrease or missing filling. The calculated color boundary C _maxL1 and the color boundary C _maxL1 corresponding to the luminance sequence interval from the luminance L _Cmaxh1 to L=100 represent the applied color boundary of the hue phase plane.

The above calculation results are stored as a database, and the data is sorted by the first sequence of hue, phase, plane, brightness, and a total of 36360 rows.

According to a specific embodiment of the present invention, step (2) is performed after step (1): calling the visual perception color space hue phase plane color equivalent boundary database of visible light.

As another preferred technical solution of the present invention, the visible light color visual perception space hue phase plane color equivalent boundary database first passes through the visible light color visual perception space hue phase plane color maximum saturation calculation module in the system of the present invention and the visible light color visual perception space The calculation module of the color equivalent boundary calculation module of the hue phase plane is completed, and the calculation includes:

(2-1) The calculation of the maximum color saturation of the hue phase plane in the visible light color visual perception space is performed by the maximum saturation calculation module of the hue phase plane color in the visible light color visual perception space:

(2-1-1) The calculation of the hue phase plane 38°-317° is performed by the calculation unit of the hue phase plane 38°-317°:

Use the x, y and z values of the wavelength 380nm to 780nm interval 5nm in the CIE 1931XYZ standard chromaticity observer spectral tristimulus value, and the white field CIEXYZ tristimulus value of the computer color space application, and calculate L and C with the method recommended by CIE and the value of h, and round and combine the hue phase plane with the h value, take the L and C values of the hue phase plane within the hue interval of 38° to 317°, and linearly interpolate the L and C values of the blank white phase plane, and use L The sum C value is used as the maximum saturation C _maxh2 and the brightness L _Cmach2 of the corresponding hue phase plane in the visible light color visual perception space.

(2-1-2) The calculation of the hue phase plane 0°-37° and 318°-359° is performed by the calculation unit of the hue phase plane 0°-37° and 318°-359°:

Calculate the L, C, and h parameters of all red, green, and blue primary color synthetic colors based on the standard parameters of the red, green, and blue primary colors in the CIERGB color space and the CIEXYZ tristimulus value of the white field applied in the computing device color space, including:

Apply the same method in the above step (1) to calculate the 3×3 matrix coefficients with the nominal CIERGB space RGB three primary color chromaticity parameters and D65 white field parameters:

X _{r, max} = 0.4108 Y _{r, max} = 0.1481 Z _{r, max} = 0

X _{g, max} = 0.3210 Y _{g, max} = 0.8401 Z _{g, max} = 0.0105

X _{b, max} = 0.2185 Y _{b, max} = 0.0118 Z _{b, max} = 1.0783

Among the merged hue planes, the hue planes from 0° to 37° and 318° to 359° are selected, and the values of the maximum saturation C _maxh2 and the brightness L _Cmach2 are extracted.

(2-2) The calculation of the color equivalent boundary of the hue phase plane in the visible light color visual perception space is performed by the C _maxLd calculation module of the color equivalent boundary C maxLd of the hue phase plane in the visible light color visual perception space:

(2-2-1) The maximum saturation ratio and optimization calculation of the hue phase surface between the visible light color visual perception space and the device color visual perception space, and the maximum saturation ratio and optimization of the hue phase surface between the visible light color visual perception space and the device color visual perception space The computing unit executes:

Select the 0° to 359° hue phase plane respectively, and calculate the ratio value B _maxh of the visible light color space C _maxh2 to the device color space C _maxh1 :

B _maxh = C _maxh2 /C _maxh1

Set the proportional coefficient F _X1 and the equivalent coefficient F _X2 , wherein, the value range of F _X1 is 2.50-6.00, and the value range of F _X2 is 1.50-2.50, and the normalized equivalent ratio B _maxd of B _maxh is calculated:

The normalized calculation of the hue plane whose B _maxh value is greater than F _X1 :

B _maxd ＝(B _maxh -F _X1 )/(maximum ratio value of the ratio interval-F _X1 )×0.1+F _X2

The normalized calculation of the hue phase plane whose B _maxh value is less than or equal to F _X1 and greater than (F _X1 -1):

B _maxd ＝(B _maxh -(F _X1 -1))/(maximum ratio value of the ratio interval-(F _X1 -1))×0.05+(F _X2 -0.05)

The normalized calculation of the hue plane whose B _maxh value is less than or equal to (F _X1 -1) and greater than (F _X1 -1.5):

B _maxd ＝(B _maxh -(F _X1 -1.5))/(maximum ratio value of the ratio interval-(F _X1 -1.5))×0.05+(F _X2 -0.1)

The normalized calculation of the hue phase plane whose B _maxh value is less than or equal to (F _X1 -1.5) and greater than (F _X1 -2):

B _maxd ＝(B _maxh -(F _X1 -2))/(maximum ratio value of the ratio interval-(F _X1 -2))×0.1+(F _X2 -0.2)

The normalized calculation of the hue plane whose B _maxh value is less than or equal to (F _X1 -2) and greater than (F _X1 -2.5):

B _maxd ＝(B _maxh -(F _X1 -2.5))/(maximum ratio value of the ratio interval-(F _X1 -2.5))×0.05+(F _X2 -0.25)

The normalized calculation of the hue plane whose B _maxh value is equal to (F _X1 -2.5) and below:

B _maxd ＝B _maxh / the maximum ratio value of the ratio interval×0.05+(F _X2 -0.3)

(2-2-2) The calculation of the maximum saturation equivalent of the hue phase plane of the visible light color space and the color boundary is performed by the calculation unit of the maximum saturation equivalent C _maxhd of the hue phase plane of the visible light color space and the color equivalent boundary C _maxLd :

Calculation of the maximum saturation equivalent C _maxhd of the hue plane:

C _maxhd = C _maxh1 × B _maxd

Calculation of the color equivalent boundary of the hue plane:

On the Cartesian coordinate plane where the abscissa is the saturation C and the ordinate is the brightness L, use the values of C _maxh2 and L _Cmaxh2 to punctuate D _maxh2 , and mark C _maxhd on the line connecting D _maxh2 to L=100, and its ordinate value The rounded brightness sequence value is recorded as L _Cmaxhd , and its coordinate point is recorded as D _maxhd ;

Mark the device color boundary of the same hue phase plane on the coordinate plane, and then mark the line connecting D _maxhd to L=0, and calculate the maximum saturation value of the corresponding luminance sequence in its luminance range, which is smoothly decreasing. The saturation value C _maxhd , this part of the value and the saturation value C _maxhd on the line from D _maxhd to L=100 constitute the equivalent boundary of the color of the visible light hue phase plane. The above calculation results are stored as a database, and the data is sorted by the first sequence of hue, phase, plane, brightness, and a total of 36360 rows.

According to a specific embodiment of the present invention, step (3) is that the red, green and blue primary color values of the color digital image are converted into CIELAB space L, C and h values and merged into the corresponding hue phase plane and brightness sequence. Call the forward conversion of the color digital image pixel color mode in the system of the present invention and merge the hue phase plane and brightness sequence module calculation:

(3-1) The calculation of converting the RGB value of the color digital image pixel color to the L, C and h values of CIELAB space is performed by the calculation unit that converts the pixel color RGB value of the color digital image into the L, C and h value of CIELAB space :

Using the nominal image display device or the corresponding parameters of the white field and red, green and blue primary colors embedded in the image itself, the standard algorithm recommended by CIE is used to convert the color synthesized by the red, green and blue primary colors of the image pixels into CIEXYZ tristimulus values and CIELAB space L, C and h values, including:

Apply the nominal sRGB space RGB primary color chromaticity and D65 white field parameters to calculate the brightness value of the maximum saturation of the RGB primary colors:

Y _{r, max} = 0.2126 Y _{g, max} = 0.7152 Y _{b, max} = 0.0722

Applying the above values calculates the 3×3 matrix coefficients:

$\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 0.4124</span>}& \mathrm{<span\; class="notranslate">\; 0.3576</span>}& \mathrm{<span\; class="notranslate">\; 0.1805</span>}\\ \mathrm{<span\; class="notranslate">\; 0.2126</span>}& \mathrm{<span\; class="notranslate">\; 0.7152</span>}& \mathrm{<span\; class="notranslate">\; 0.0722</span>}\\ \mathrm{<span\; class="notranslate">\; 0.0193</span>}& \mathrm{<span\; class="notranslate">\; 0.1192</span>}& \mathrm{<span\; class="notranslate">\; 0.9505</span>}\end{array}\right]$

To display an image on a non-standard device, it is necessary to calculate the CIEXYZ tristimulus value of the white point of the device and the maximum saturation of the three primary colors of red, green and blue:

Using a standard spectrophotometer, measure the white field tristimulus values X _w ', Y _w ', and Z _w ' of the equipment according to conventional specifications, and calculate the white field normalization coefficient K ₁ :

K ₁ =100/Y _w '

Calculate the CIEXYZ tristimulus value for the white field of the device:

X _w =X _w '×K ₁ , Y _w =Y _w '×K ₁ , Z _w =Z _w '×K ₁

Using a standard spectrophotometer, measure the tristimulus values under the condition of maximum saturation of red, green and blue primary colors, X _r ', Y _r ' and Z _r ', X _g ', Y _g ' and Z _g respectively according to conventional specifications ', X _b ', Y _b ' and Z _b ' respectively calculate the CIEXYZ tristimulus values of the three primary colors:

X _r,max =X _r '×K ₁ , Y _r,max =Y _r '×K ₁ , Z _r,max =Z _r '×K ₁

X _g,max =X _g '×K ₁ , Y _g,max =Y _g '×K ₁ , Z _g,max =Z _g '×K ₁

X _b,max =X _b '×K ₁ , Y _b,max =Y _b '×K ₁ , Z _b,max =Z _b '×K ₁

Use the CIEXYZ tristimulus values of the three primary colors calculated above to replace the 3×3 matrix coefficients in the above-mentioned standard method, and use the calculated white field CIEXYZ tristimulus values to replace the nominal white field CIEXYZ tristimulus values of the above-mentioned equipment, and calculate CIELAB space L, C and h values.

(3-2) The merging calculation of the pixel color hue phase plane and the luminance sequence is performed by the pixel color hue phase plane and the luminance sequence merging unit:

Divide the image color space into a total of 360 reference color phase planes from 0 to 359, round up and merge them into the corresponding reference color phase planes based on the h value of the image pixel color hue, and divide the brightness L range in the hue phase plane into 101 reference planes from 0 to 100 in total Sequence, the value of color brightness L is rounded and merged into the corresponding brightness sequence, so that the pixel color parameters can be represented by integer hue h, integer brightness L and saturation C with four decimal places, which is one of the calculation conditions for saturation expansion.

According to a specific embodiment of the present invention, step (4) is to calculate the image pixel color saturation extension. Call the image pixel color saturation value expansion module in the system of the present invention, and calculate and comprise:

(4-1) Calculate the pixel color saturation mapping inflection point C _gL , which is executed by the saturation mapping inflection point C _gL calculation unit:

C _gL ＝C _maxL1 *X _S1

C _maxL1 is the maximum saturation value retrieved and called from the hue phase plane database of the device's visual perception color space with the integer value of the pixel hue phase plane and brightness sequence. X _S1 is the inflection point function set according to the needs, and the value range is 0.65-0.95, typically 0.8 -0.9;

(4-2) Calculate the pixel color saturation expansion ratio BI _L , which is executed by the pixel color saturation expansion ratio BI _L calculation unit:

BI _L =C _maxLd /C _maxL1

C _maxLd is the equivalent saturation value retrieved and called from the hue phase plane color equivalent boundary database of the visible light visual perception color space with the integer value of the pixel hue phase plane and brightness sequence;

It is allowed to call multiple saturation expansion ratio control submodules in the system of the present invention:

(4-2-1) Set the sub-module of saturation expansion ratio control:

The calculation includes setting a plurality of saturation expansion ratio adjustment coefficients BI _Kx , with a value range of 0.00-1, taking BI _LY = BI _Kx × BI _L as the saturation expansion ratio for actual pixel application, typically, the BI _K2 of a character image is typically 0.10 -0.40, landscape image BI _K1 is typically 0.40-0.90;

(4-2-2) Set the sub-module of the boundary of the hue plane interval:

The calculation includes setting multiple sets of hue plane intervals, hue interval boundaries H _Dx and H _Gx , the value range is 0°-359°, using a different saturation expansion ratio BI _LY for colors in the specified hue interval than those outside the interval, and character images Typical hue intervals _HD1 = 340° and H _G1 = 100°, within this hue interval, BI _LY =BI _K2 ×BI _L ; outside this hue interval, BI _LY =BI _K1 ×BI _L . Set a transitional hue interval inside the boundary of the hue interval to control the smooth change of the saturation expansion ratio inside and outside the specified hue interval. The value range of the transition area is 0-20;

(4-2-3) Set the sub-module of the saturation ratio threshold:

The calculation includes setting the saturation ratio value as the threshold value C _Gx , the numerical range is 0.00-1, and the threshold value is used as the saturation ratio boundary of colors using different saturation expansion ratios in the same brightness sequence. Typically, the character image setting C _Gx is typically 0.50- 0.75, the color below the threshold, BI _LY =BI _K2 ×BI _L , the color above the threshold, BI _LY =BI _K1 ×BI _L . A transition zone is set on the side above the threshold to control the saturation expansion ratio to change smoothly, and the value range of the transition zone is 0.00-0.2;

(4-3) Calculation of pixel color saturation _CL extension, from pixel color saturation _CL expansion to C _LN calculation unit execution:

C _LN =C _L *BI _LY ,

BI _LY is regulated by the above three saturation expansion ratio control settings. If the calculation result C _LN is greater than C _gL , the mapping is calculated:

C _LN ＝C _gL +(C _LN -C _gL )/(C _maxLd -C _gL )*(C _maxL1 -C _gL )

According to a specific embodiment of the present invention, step (5) is to calculate the values of image pixel colors L, h, and saturation C _LN as normative R, G, and B values. According to the preferred technical solution of the present invention, calling the image pixel color mode inverse transformation and standardization module in the system of the present invention, the calculation includes:

Using the standard method recommended by CIE, the calculated and unchanged luminance L, hue angle h value, and C _LN value obtained after calculation and expansion are calculated as the red, green and blue primary color values of the device. This algorithm is the inverse operation of the above step (3). The white field CIEXYZ tristimulus value required to calculate the CIEXYZ tristimulus value from the pixel color CIELAB parameter is the same as the forward calculation, and 3× is required to calculate the RGB value from the pixel color CIEXYZ tristimulus value. The 3 matrix coefficients are obtained by inverting the matrix 3×3 coefficients used in the above step (3):

$\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 3.2406</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.9689</span>}& \mathrm{<span\; class="notranslate">\; 0.0557</span>}\\ <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1.5372</span>}& \mathrm{<span\; class="notranslate">\; 1.8758</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.2040</span>}\\ <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.4986</span>}& \mathrm{<span\; class="notranslate">\; 0.0415</span>}& \mathrm{<span\; class="notranslate">\; 1.0570</span>}\end{array}\right]$

The calculated R, G, and B values are respectively rounded, and values greater than 255 are normalized to 255, and values less than 0 are normalized to 0.

A typical digital image composed of three primary colors of red, green and blue, taking a landscape image as an example, after adjusting the visual perception saturation parameter of each pixel color according to the FECr algorithm of the present invention, compared with the original saturation value of the image, it can be seen that After the FECr algorithm is adjusted, the color saturation value increases by an average of about 52%, and the saturation of about 50% of the pixels in the image increases by 70%, 30% of the pixels increases by 70-30%, 10% of the pixels increases by 30-10%, 10% % pixels increased by 10-0%. The relationship between the ratio of the pixel color saturation value before and after adjustment to the maximum saturation value of the merged hue phase brightness sequence shows the non-linearity of the actual increment of saturation.

According to the FECr algorithm of the present invention, under the condition that the color space boundary of the device displaying the image is not expanded, the more visually sensitive colors in the image are calculated to increase the visual perception characteristics of relatively more visible light colors, and the relatively less sensitive colors are calculated to increase less or remain unchanged, this non-linear enhancement method can make viewing images relatively significantly closer to viewing the actual scene in the visible light color space.

Description of drawings

Figure 1 is the isoenergetic spectrum trajectory calculated by the spectral tristimulus values of the CIE1931XYZ system and marked on the xy coordinate plane of the CIE xyY space, the abscissa is x, and the ordinate is y.

Fig. 2-1 is a flowchart of a method for visually perceptual high-fidelity transformation of a color digital image according to a specific embodiment of the present invention, illustrating the calculation process of the color boundary of the device's visual perception space, hue phase plane;

Fig. 2-2 is a flow chart of a method for visually perceptual high-fidelity transformation of a color digital image according to a specific embodiment of the present invention, illustrating the calculation process of the color equivalent boundary of the visible light color visually perceptual space hue phase plane;

2-3 are flow charts of a method for performing high-fidelity visual perception transformation on a color digital image according to a specific embodiment of the present invention, illustrating the high-fidelity visual perception transformation calculation process of the pixel color of a color digital image.

The red circle in Figure 3 shows an example of the color visual perception space of non-standard equipment. The color coordinates of the hue and phase planes are all merged. The gray line in the middle represents the algorithm for merging brightness sequences _. Smoothly decrease and make up for missing calculation results, saturation C on the abscissa, brightness L on the ordinate.

Figure 4 shows the relevant color boundaries of the hue phase plane in the color visual perception space, the abscissa is saturation C, and the ordinate is lightness L, for example h=110° hue phase plane, where the blue point represents the maximum saturation C _maxh1 of the device, and the blue line Indicates the color boundary C _maxL1 , the red dot in the black circle indicates the maximum saturation equivalent of visible light color C _maxhd , the black dotted line indicates the equivalent color boundary C _maxLd , the red dot indicates the maximum saturation C _maxh2 of visible light color, and the red line indicates its schematic boundary.

Figure 5 shows the algorithm for calculating the saturation extension of pixel color visual perception. In the figure, the C _L point represents the pixel color coordinate, the C _gL point represents the saturation mapping inflection point, and the red line represents the luminance sequence of color merging, and the rest are the same as in Fig. 4 .

Figure 6 and Figure 7 show the images before and after processing by the method of the present invention, the right side of the color image is the original image, and the left side is the image after the FECr algorithm calculates the saturation C and expands.

Figure 8-1 Typical system flow using the FECr algorithm computer program.

Figure 8-2 Typical system flow of TV using FECr algorithm IP.

Figure 8-3 Typical system flow of TV using FECr algorithm ASIC.

Figure 8-4 Typical system flow of electronic equipment using FECr algorithm ASIC.

Detailed ways

Example 1

Implement the high-fidelity conversion process of color digital image pixel color visual perception of the present invention.

(1) Computing the device color visual perception space hue phase plane color boundary database.

The database is completed through the calculation module of the device color visual perception space hue phase plane color boundary calculation module in the system of the present invention, and the calculation includes:

(1-1) Transform the red, green and blue primary color values of the device color space into the calculation of CIELAB space L, C and h values, and transform the red, green and blue primary color values of the device color space into CIELAB space L, C and h The value computation unit executes:

Electronic equipment with image display function is typically sRGB color space and D65 white field, sRGB space RGB three primary color chromaticity check:

x _{r, max} = 0.64 y _{r, max} = 0.33, x _{g, max} = 0.30 y _{g, max} = 0.60, x _{b, max} = 0.15 y _{b, max} = 0.06

D65 white field CIEXYZ tristimulus value lookup:

X _w =0.950456 Y _w =1 Z _w =1.089058

Calculate the 3×3 matrix coefficients required for color transformation from the above parameters:

Use the RGB values of the three primary colors of the device's white field and the CIEXYZ tristimulus values and the forward transformation formula:

$\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; 0.9505</span>}\\ \mathrm{<span\; class="notranslate">\; 1.0000</span>}\\ \mathrm{<span\; class="notranslate">\; 1.0891</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}& {\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}& {\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}\\ {\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}& {\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}& {\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}\\ {\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}& {\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}& {\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="max"\; filenames="HDA0000140464510000051.png"\; state="\{\{state\}\}">max</figure-callout></span>}}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right]$

Express the 3×3 matrix coefficients as the product of the chromaticity value of the three primary colors and the brightness value:

$\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; 0.9505</span>}\\ \mathrm{<span\; class="notranslate">\; 1.0000</span>}\\ \mathrm{<span\; class="notranslate">\; 1.0891</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}& <span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}& <span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}\\ {\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}& {\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}& {\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}\\ <span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}& <span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}& <span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="max"\; filenames="HDA0000140464510000051.png"\; state="\{\{state\}\}">max</figure-callout></span>}}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right]$

The brightness value of the maximum saturation of the RGB three primary colors is calculated by the above equation:

Y _{r, max} = 0.2126 Y _{g, max} = 0.7152 Y _{b, max} = 0.0722

Apply the above values to calculate the matrix 3×3 coefficients:

$\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 0.4124</span>}& \mathrm{<span\; class="notranslate">\; 0.3576</span>}& \mathrm{<span\; class="notranslate">\; 0.1805</span>}\\ \mathrm{<span\; class="notranslate">\; 0.2126</span>}& \mathrm{<span\; class="notranslate">\; 0.7152</span>}& \mathrm{<span\; class="notranslate">\; 0.0722</span>}\\ \mathrm{<span\; class="notranslate">\; 0.0193</span>}& \mathrm{<span\; class="notranslate">\; 0.1192</span>}& \mathrm{<span\; class="notranslate">\; 0.9505</span>}\end{array}\right]$

Apply the above matrix coefficients to calculate the conversion of the device color RGB values to CIEXYZ space X, Y and Z values, and then apply the D65 white field tristimulus value to calculate the conversion of color XYZ values to CIELAB space L, C and h values.

A typical device is composed of 8 bits of each of the three primary colors of red, green and blue, that is, 2 ^{3 × 8,} a total of 16777216 color scalars, which are transformed by the above calculations in turn.

Non-standard equipment calculates the CIEXYZ tristimulus value of the white point and the maximum saturation of the three primary colors of red, green and blue:

Using a standard spectrophotometer, measure the white field tristimulus values X _w ', Y _w ', and Z _w ' of the equipment according to conventional specifications, and calculate the white field normalization coefficient K ₁ :

K ₁ =100/Y _w '

Calculate the CIEXYZ tristimulus value for the white field of the device:

X _w =X _w '×K ₁ , Y _w =Y _w '×K ₁ , Z _w =Z _w '×K ₁

Using a standard spectrophotometer, measure the tristimulus values under the condition of maximum saturation of red, green and blue primary colors, X _r ', Y _r ' and Z _r ', X _g ', Y _g ' and Z _g respectively according to conventional specifications ', X _b ', Y _b ' and Z _b ', and then calculate the CIEXYZ tristimulus values of the three primary colors:

X _r,max =X _r '×K ₁ , Y _r,max =Y _r '×K ₁ , Z _r,max =Z _r '×K ₁

X _g,max =X _g '×K ₁ , Y _g,max =Y _g '×K ₁ , Z _g,max =Z _g '×K ₁

X _b,max =X _b '×K ₁ , Y _b,max =Y _b '×K ₁ , Z _b,max =Z _b '×K ₁

Use the above-calculated CIEXYZ tristimulus values at the maximum saturation of the three primary colors to replace the 3×3 matrix coefficients in the above-mentioned standard method, and use the calculated white-field CIEXYZ tri-stimulus values to replace the above-mentioned equipment nominal white-field CIEXYZ three Stimulus values, using the standard method described above, sequentially transform the R, G, and B values of a total of 16,777,216 colors into CIELAB space L, C, and h values.

(1-2) The calculation of the color boundary of the hue phase plane in the device color visual perception space is performed by the color boundary extraction unit of the hue phase plane in the device color visual perception space.

Integers from 0 to 359 are used to represent the reference hue plane, the hue h value is used to round up and merge the device colors into the corresponding reference hue phase plane, integers from 0 to 100 are used to represent the reference brightness sequence, and the L value is used to round and merge the colors into the corresponding brightness sequence. The maximum saturation value C _maxL1 in each brightness sequence of the hue phase plane is extracted as the basis for calculating the color boundary of the hue phase plane.

(1-3) The smoothing calculation of the color boundary of the hue phase plane is performed by the color boundary C _maxL1 smoothing unit of the hue phase plane:

Extract the color boundary corresponding to the brightness sequence range from the brightness L _Cmaxh1 to the lowest brightness L=0 of the maximum saturation value C _maxh1 in the hue phase plane, calculate the smooth boundary with a standard linear interpolation algorithm, and trim the maximum saturation C of the original brightness sequence _maxL1 non-smooth decrement or missing filling. The calculated color boundary C _maxL1 of this part, together with the color boundary C _maxL1 corresponding to the luminance sequence interval from luminance L _Cmaxh1 to L=100, represent the final applied color boundary of the hue phase plane.

The so-called smoothness in the above algorithm means that the color brightness sequence is arranged from high to low, and its saturation C _maxL1 is smaller than the above sequence and greater than all the following sequences. If the saturation is less than the calculated value, the calculated value is used, and if it is greater than the calculated value, it remains unchanged.

Store the above calculation results as a database. The data is sorted according to the first order of hue phase plane h order of brightness order L, a total of 36360 lines. The calculation process of the above step (1) is shown in Figure 2-1.

(2) Calculate the color equivalent boundary database of the hue phase plane color visual perception space of visible light color.

The visible light color visual perception space hue phase plane color equivalent boundary database is completed through the calculation module of the visible light color visual perception space hue phase plane color maximum saturation calculation module and the visible light color visual perception space hue phase plane color equivalent boundary calculation module in the system of the present invention, Calculations include:

(2-1) Calculation of the maximum color saturation of the color phase plane in the visible light color visual perception space,

(2-1-1) The calculation of the hue phase plane 38°-317° is performed by the calculation unit of the hue phase plane 38°-317°:

Calculate the values of L, C and h in CIELAB space with the method recommended by CIE by using the x, y and z values of the wavelength 380nm to 780nm interval of 5nm in the CIE 1931XYZ standard chromaticity observer spectral tristimulus value, and the D65 white field CIEXYZ tristimulus value , and the hue plane is rounded and merged with the h value, and the L and C values of the hue phase plane within the hue interval of 38° to 317° are taken, and the L and C values of the blank white phase plane are calculated linearly, and the L and C values are used The value is taken as the maximum saturation C _maxh2 and its brightness L _Cmach2 of the corresponding hue phase plane in the visible light color visual perception space.

(2-1-2) The calculation of the hue phase plane 0°-37° and 318°-359° is performed by the calculation unit of the hue phase plane 0°-37° and 318°-359°:

Apply CIERGB space RGB primary color chromaticity parameters:

x _{r, max} = 0.735 Y _{r, max} = 0.265, x _{g, max} = 0.274 Y _{g, max} = 0.717, x _{b, max} = 0.167 Y _{b, max} = 0.009

As well as the D65 white field parameters, the 3×3 matrix coefficients are obtained by calculating the same method in step (1):

X _{r, max} = 0.4108 Y _{r, max} = 0.1481 Z _{r, max} = 0

X _{g, max} = 0.3210 Y _{g, max} = 0.8401 Z _{g, max} = 0.0105

X _{b, max} = 0.2185 Y _{b, max} = 0.0118 Z _{b, max} = 1.0783

Calculate the L, C and h parameters of all colors synthesized by the three primary colors of red, green and blue in CIERGB space with the above matrix coefficients and D65 white field CIEXYZ tristimulus value. In the merged hue plane, select 0° to 37° and 318 ° to 359° hue phase plane, and extract the maximum saturation C _maxh2 and its brightness L _Cmach2 value.

(2-2) The calculation of the color equivalent boundary of the hue phase plane in the visible light color visual perception space is performed by the C _maxLd calculation module of the color equivalent boundary C maxLd of the hue phase plane in the visible light color visual perception space:

(2-2-1) The maximum saturation ratio and optimization calculation of the hue phase surface between the visible light color visual perception space and the device color visual perception space, and the maximum saturation ratio and optimization of the hue phase surface between the visible light color visual perception space and the device color visual perception space The computing unit executes:

Select the 0° to 359° hue phase plane, and calculate the ratio value B _maxh of the visible light color space C _maxh2 and the device color space C _maxh1 respectively:

B _maxh = C _maxh2 /C _maxh1

The inspection found that the maximum value of B _maxh was close to 5, and the application found that the sRGB color space of the device should not be directly applied to the proportional value B _maxh to expand the color saturation.

Set the proportional coefficient F _X1 and the equivalent coefficient F _X2 , where the value of F _X1 is 4.5, and the value of F _X2 is 1.9, and the normalized equivalent value B _maxd of B _maxh is calculated:

The normalized calculation of the hue plane whose B _maxh value is greater than F _X1 :

B _maxd ＝(B _maxh -F _X1 )/(maximum ratio value of the ratio interval-F _X1 )*0.1+F _X2

The normalized calculation of the hue phase plane whose B _maxh value is less than or equal to F _X1 and greater than (F _X1 -1):

B _maxd ＝(B _maxh -(F _X1 -1))/(maximum ratio value of the ratio interval-(F _X1 -1))*0.05+(F _X2 -0.05)

The normalized calculation of the hue plane whose B _maxh value is less than or equal to (F _X1 -1) and greater than (F _X1 -1.5):

B _maxd ＝(B _maxh -(F _X1 -1.5))/(maximum ratio value of the ratio interval-(F _X1 -1.5))*0.05+(F _X2 -0.1)

The normalized calculation of the hue phase plane whose B _maxh value is less than or equal to (F _X1 -1.5) and greater than (F _X1 -2):

B _maxd ＝(B _maxh -(F _X1 -2))/(maximum ratio value of the ratio interval-(F _X1 -2))*0.1+(F _X2 -0.2)

The normalized calculation of the hue plane whose B _maxh value is less than or equal to (F _X1 -2) and greater than (F _X1 -2.5):

B _maxd ＝(B _maxh -(F _X1 -2.5))/(maximum ratio value of the ratio interval-(F _X1 -2.5))*0.05+(F _X2 -0.25)

The normalized calculation of the hue plane whose B _maxh value is equal to (F _X1 -2.5) and below:

B _maxd = B _maxh / maximum ratio value of the ratio interval * 0.05+(F _X2 -0.3)

(2-2-2) The calculation of the maximum saturation equivalent of the hue phase plane and the color equivalent boundary in the visible light color visual perception space is performed by the calculation unit of the maximum saturation equivalent C _maxhd of the hue phase plane of the visible light color visual perception space and the color equivalent boundary C _maxLd :

Calculation of the maximum saturation equivalent C _maxhd of the hue plane:

C _maxhd = C _maxh1 × B _maxd

Calculation of the color equivalent boundary of the hue plane:

On the Cartesian coordinate plane where the abscissa is saturation C and the ordinate is lightness L, punctuate D _maxh2 with C _maxh2 and L _Cmaxh2 values, and mark C _maxhd on the line connecting D _maxh2 to L=100, and its ordinate value is rounded The luminance sequence value of is recorded as L _Cmaxhd , and its coordinate point is recorded as D _maxhd .

On the coordinate plane, mark the device color boundary of the same hue phase plane, and then mark the line connecting D _maxhd to L=0, and calculate the maximum saturation value of the corresponding brightness sequence in the brightness interval to smoothly decrease to accommodate the device color boundary. The saturation value C _maxLd of , this part of the value and the saturation value C _maxLd on the line from D _maxhd to L=100 constitute the equivalent boundary of the color of the visible light hue phase plane.

For the above-mentioned algorithm to accommodate the saturation required by the device color boundary, the maximum saturation value of the inflection point brightness sequence on the device color boundary is increased by 2-3%.

The above calculation results are stored as a database, which has the same format and sorting as the above device color database.

(2-3) Calculation of the maximum saturation ratio between the visible light color equivalent space and the device color space hue phase plane:

Retrieve the maximum saturation C _maxh1 and C _maxhd of the hue plane from the above two databases to calculate the ratio of the two, establish the database, sort by the hue plane h, and the data is 360 lines. The calculation process of the above step (2) is shown in Figure 2-2.

(3) Complete the conversion of the red, green and blue primary color values of the color digital image into CIELAB space L, C and h values and merge them into the corresponding hue phase plane and brightness sequence, and forward conversion and merge color phase of the pixel color mode of the color digital image facet and brightness sequence modules are executed.

Calculations include:

(3-1) The calculation of the conversion of color digital image pixel color RGB value into the L, C and h value of CIELAB space is carried out by the calculation unit that the color digital image pixel color RGB value is converted into CIELAB space L, C and h value:

Using the nominal value of the device displaying the image or the corresponding parameters of the white point and the three primary colors of red, green and blue embedded in the image itself, apply the standard algorithm recommended by CIE to convert the color represented by the three primary colors of red, green and blue of the image pixel into the CIEXYZ tristimulus value And CIELAB space L, C and h values, the algorithm program and related parameters are the same as step (1).

To display an image on a non-standard device, it is necessary to measure and calculate the CIEXYZ tristimulus value at the white point of the device and the maximum saturation of the three primary colors of red, green and blue. The algorithm program and related parameters are the same as in step (1).

(3-2) The merging calculation of the pixel color hue phase plane and the luminance sequence is performed by the pixel color hue phase plane and the luminance sequence merging unit:

Divide the image color space into a total of 360 reference color phase planes from 0 to 359, round off the hue h value and enter the corresponding reference color phase plane, divide the brightness L range in the hue phase plane into 101 reference sequences from 0 to 100, and use The brightness L value is rounded and merged into the corresponding brightness sequence, so that the pixel color parameter can be represented by integer hue h, integer brightness L and saturation C with four decimal places, creating one of the calculation conditions for saturation expansion.

(4) Calculate pixel color saturation expansion, which is executed by the image pixel color saturation value expansion module.

Calculations include:

(4-1) Calculate the pixel color saturation mapping inflection point C _gL , which is executed by the saturation mapping inflection point C _gL calculation unit:

C _gL ＝C _maxL1 *X _S1

Set the inflection point function X _S1 to 0.85;

(4-2) Calculate the pixel color saturation expansion ratio BI _L , which is executed by the pixel color saturation expansion ratio BI _L calculation unit:

BI _L =C _maxLd /C _maxL1

Call multiple saturation expansion ratio control submodules in the system of the present invention:

(4-2-1) Set the saturation expansion ratio control coefficient:

The calculation includes setting a plurality of saturation expansion ratio adjustment coefficients BI _Kx , with a value range of 0.00-1, and taking BI _LY =BI _Kx ×BI _L as the saturation expansion ratio actually applied to the pixel.

Typically, BI _K1 =0.75 is used for images in which scenery is the main content, and BI _K2 =0.15 is used for images in which people are the main content.

(4-2-2) Set the boundary of the specified hue plane interval:

The calculation includes setting multiple pairs of specified hue plane interval boundaries _HDx and H _Gx , with a value range of 0°-359°. Typically, the character image is set to H _D1 =340° and H _G1 =100°, and the hue angle h≤H _G1 or h≥H _D1 is applied to the hue plane color BI _LY =BI _K2 ×BI _L , and the remaining colors are applied to BI _LY =BI _K1 ×BI _L . Set the transition hue interval value inside the boundary of the hue interval to 10.

(4-2-3) Set the saturation ratio threshold:

The calculation includes setting a plurality of saturation ratio thresholds C _Gx with a value range of 0.00-1. Typically, the character image C _G1 =0.6, for the above-specified h≤H _G1 or h≥H _D1 hue interval color, the saturation ratio is lower than the threshold, BI _LY =BI _K2 ×BI _L , the saturation ratio is higher than the threshold, BI _LY =BI _K1 ×BI _L . A transition zone is set on the side above the threshold, and the range of the transition zone is set to 0.1.

(4-3) Calculation of pixel color saturation _CL extension, from pixel color saturation _CL expansion to C _LN calculation unit execution:

C _LN = C _L *BI _LY ,

Judgment, if C _LN is greater than C _gL , the mapping must be calculated:

C _LN ＝C _gL +(C _LN -C _gL )/(C _maxLd -C _gL )*(C _maxL1 -C _gL )

(5) Calculate the image pixel color L, h, and saturation C _LN values as R, G, and B values.

Call the inverse transformation and normalization module of the image pixel color mode, and the calculation includes:

Using the standard method recommended by CIE, the red, green and blue primary color values of the image are calculated from the L and h values obtained by the forward transformation of the image pixel color and the C _LN value obtained after calculation and expansion. This algorithm is the inverse calculation of the above step (3). The white field required to calculate the CIEXYZ tristimulus value from the pixel color CIELAB space parameter is also D65, and the 3×3 matrix coefficient required to calculate the RGB value from the pixel color CIEXYZ tristimulus value is given by The inversion of the matrix 3×3 coefficients used in the above step (3) is obtained:

$\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 3.2406</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.9689</span>}& \mathrm{<span\; class="notranslate">\; 0.0557</span>}\\ <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1.5372</span>}& \mathrm{<span\; class="notranslate">\; 1.8758</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.2040</span>}\\ <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.4986</span>}& \mathrm{<span\; class="notranslate">\; 0.0415</span>}& \mathrm{<span\; class="notranslate">\; 1.0570</span>}\end{array}\right]$

The calculated R, G, and B values were rounded to integers, and the values greater than 255 were normalized to 255, and the values less than 0 were normalized to 0. The calculation process of the above steps (3) (4) (5) is shown in Figure 2-3.

All the pixels of the image sequentially execute the above-mentioned FECr algorithm flow of the color digital image to complete the calculation, and the image has completed the high-fidelity transformation of visual perception.

Embodiment 2 uses the typical system of FECr algorithm of the present invention

11. The computer hard disk HD is used as a typical carrier for submitting the FECr algorithm. The carrier with the same function also includes CD, DVD, U disk, etc., and the FECr algorithm is called by the network with authorization. The FECr algorithm is called by the computer CPU+GPU in a program mode and runs in RAM. Color digital images are stored in the computer hard disk and called by the FECr algorithm program, and then stored back to the hard disk after being processed by the FECr algorithm. Image data can be copied in CD, DVD, U disk and other carriers and another hard disk, and can also be transmitted to a designated location through the network.

The FECr algorithm program can process single frame images and frame sequence images. The format of the single frame image may be an uncompressed format such as .GIF, .bmp, or a compressed format such as .jpg. The frame sequence image format can be common .MOV, .AVI, etc., and special I/O can also be used to process related format files. The number of monitors for real-time viewing of the image effect transformed by the FECr algorithm can be configured as required. The system is shown in Figure 8-1.

1.2. The TV main chip is used as a typical application of the FECr algorithm IP. The R, G and B color scale lookup tables used by the gamma correction module in the IP can be adjusted according to the special gamma settings in the TV main chip. The system is shown in Figure 8-2.

1.3. The TV is used as a typical application of the FECr algorithm ASIC, and the I/O matching the TV is set to obtain the color RGB data of the video image. The system is shown in Figure 8-3.

1.4. Devices that apply the FECr algorithm ASIC also include notebook computers, tablet computers, mobile phones, game consoles, LCD monitors, computer graphics cards, etc. The system is shown in Figure 8-4.

Claims (10)

1. color digital image carries out the method for visually-perceptible high-fidelity conversion, it is characterized in that, said method comprising the steps of:

(1) obtain color digital image pixel color R, G and B numerical value L, C and the h numerical value in the CIELAB space, wherein, h is a hue angle, and L is that brightness, C are saturation;

(2) the pixel color merger gets into form and aspect plane and brightness sequence,

Get into corresponding benchmark form and aspect plane with the merger of hue angle h value, get into the corresponding bright sequence with the merger of brightness L value;

(3) expansion of completion pixel color saturation C,

(3-1) confirm pixel color saturation mapping flex point C _GL:

C _gL＝C _maxL1×X _S1

Wherein, C _MaxL1For pixel color is gone into the brightness sequence maximum saturation value of form and aspect plane, X in the merger of equipment colour vision aware space _S1Be the proportionality coefficient that is provided with, exemplary value scope 0.65-0.95;

The saturation of (3-2) calculating this brightness sequence is expanded ratio BI _L:

BI _L＝C _maxLd/C _maxL1

Wherein, C _MaxLdIntensity value for pixel color same brightness sequence on identical form and aspect plane color equivalent border, visible light colors visually-perceptible space;

(3-3) saturation expansion ratio regulation and control coefficient B IKx is set:

B _ILY＝B _IL×B _IKx

Wherein, B _IKxAllow to be provided with a plurality of, be respectively applied for specified requirements, exemplary value scope 0.00-1, B _ILYFor regulating and control back saturation expansion proportional numerical value;

(3-4) calculating pixel color saturation C _LExpansion back C _LN:

C _LN＝C _L×BI _LY

Like C _LNGreater than C _GLThen calculate its mapping:

C _LN＝C _gL+(C _LN-C _gL)/(C _maxLd-C _gL)×(C _maxL1-C _gL)；

(4) with image pixel colour brightness L, hue angle h and the saturation C that after step (3) expansion, obtains _LNNumerical computations is standard R, G and B value.

2. method according to claim 1 is characterized in that, color digital image pixel color R, G and B numerical transformation are L, C and the h parameter in the CIELAB space, obtains through following method:

2.1 white and R, G and B three primary colors regulation parameter of use equipment or image nominal are used the standard method that CIE recommends, and image pixel RGB tristimulus values is calculated as CIEXYZ tristimulus values and CIELAB Space L, C and h value, comprising:

Brightness when calculating RGB three primary colors maximum saturation with sRGB space RGB three primary colors chromaticity and white CIEXYZ tristimulus values of D65:

Y _r，max＝0.2126?Y _g，max＝0.7152?Y _b，max＝0.0722

Use the above color RGB numerical value that calculates and convert 3 * 3 required matrix coefficients of CIEXYZ tristimulus values into:

$\left[\begin{array}{ccc}0.4124& 0.3576& 0.1805\\ 0.2126& 0.7152& 0.0722\\ 0.0193& 0.1192& 0.9505\end{array}\right];$

The CIEXYZ tristimulus values when 2.2 display image needs white of computing equipment and R, G and B three primary colors maximum saturation on the non-standard equipment:

2.2.1 the standard of use spectrophotometer is according to white tristimulus values X of conventional criterion equipment _w', Y _w' and Z _w', calculate white normalization coefficient K ₁:

K ₁＝100/Y _w’

The CIEXYZ tristimulus values that computing equipment is white:

X _w＝X _w’×K ₁?Y _w＝Y _w’×K ₁?Z _w＝Z _w’×K ₁，

2.2.2 the standard of use spectrophotometer is according to the tristimulus values under the conventional standard difference measuring equipment RGB three primary colors maximum saturation condition: X _r', Y _r' and Z _r', X _g', Y _g' and Z _g', X _b', Y _b' and Z _b' calculate trichromatic CIEXYZ tristimulus values respectively:

X _r，max＝X _r’×K ₁?Y _r，max＝Y _r’×K ₁?Z _r，max＝Z _r’×K ₁

X _g，max＝X _g’×K ₁?Y _g，max＝Y _g’×K ₁?Z _g，max＝Z _g’×K ₁

X _b，max＝X _b’×K ₁?Y _b，max＝Y _b’×K ₁?Z _b，max＝Z _b’×K ₁，

2.3 with more than calculate the three primary colors maximum saturation time CIEXYZ tristimulus values; 3 * 3 matrix coefficients in the alternative steps 2.1 said standard methods; With white CIEXYZ tristimulus values alternative steps 2.1 said equipment nominal white field CIEXYZ tristimulus values that calculate, calculate CIELAB Space L, C and h numerical value.

3. method according to claim 1 is characterized in that, the form and aspect plane of said pixel color and the merger of brightness sequence obtain through following method:

With 0-359 totally 360 integers represent the reference colours phase face respectively, get into corresponding benchmark form and aspect plane with the merger that rounds up of color form and aspect h value; With 0-100 totally 101 integers represent brightness L consensus sequence in the form and aspect plane respectively, get into the corresponding bright sequence with the merger that rounds up of colour brightness L value in the form and aspect plane.

4. method according to claim 1 is characterized in that, the maximum saturation value of equipment colour vision aware space form and aspect plane brightness sequence obtains through following method:

4.1 equipment red to using, green and primary colors display color digital picture; The Bai Chang of use equipment nominal and red, green and primary colors regulation parameter; Use the standard method that CIE recommends; Equipment is red, green and primary colors are synthetic whole colors convert CIEXYZ tristimulus values and CIELAB Space L, C and h value into, comprising:

Brightness when calculating RGB three primary colors maximum saturation with sRGB space RGB three primary colors chromaticity and white CIEXYZ tristimulus values of D65:

Y _r，max＝0.2126?Y _g，max＝0.7152?Y _b，max＝0.0722

Use the above color RGB numerical value that calculates and convert 3 * 3 required matrix coefficients of CIEXYZ tristimulus values into:

$\left[\begin{array}{ccc}0.4124& 0.3576& 0.1805\\ 0.2126& 0.7152& 0.0722\\ 0.0193& 0.1192& 0.9505\end{array}\right];$

4.2 non-standard equipment needs the CIEXYZ tristimulus values of computing equipment Bai Chang and red, green and primary colors:

4.2.1 the standard of use spectrophotometer is according to white tristimulus values X of conventional criterion equipment _w', Y _w' and Z _w', calculate white normalization coefficient K ₁:

K ₁＝100/Y _w’

The CIEXYZ tristimulus values that computing equipment is white:

X _w＝X _w’×K ₁?Y _w＝Y _w’×K ₁?Z _w＝Z _w’×K ₁，

4.2.2 use the standard spectrophotometer, according to the conventional standard tristimulus values under red, the green and primary colors maximum saturation condition of measuring equipment respectively, X _r', Y _r' and Z _r', X _g', Y _g' and Z _g', X _b', Y _b' and Z _b' calculate trichromatic CIE XYZ tristimulus values more respectively:

X _r，max＝X _r’×K ₁?Y _r，max＝Y _r’×K ₁?Z _r，max＝Z _r’×K ₁

X _g，max＝X _g’×K ₁?Y _g，max＝Y _g’×K ₁?Z _g，max＝Z _g’×K ₁

X _b，max＝X _b’×K ₁?Y _b，max＝Y _b’×K ₁?Z _b，max＝Z _b’×K ₁，

4.3 with more than the primary colors CIEXYZ tristimulus values that calculates; 3 * 3 matrix coefficients in the alternative steps 4.1 said standard methods; With white CIEXYZ tristimulus values alternative steps 4.1 said equipment nominal white field CIEXYZ tristimulus values that calculate, calculate CIELAB Space L, C and h numerical value;

4.4 with the armamentarium spatial color that calculates, get into corresponding benchmark form and aspect plane, get into the corresponding bright sequence with the merger that rounds up of brightness L value again with the merger that rounds up of form and aspect h value;

4.5 maximum saturation C with the whole brightness sequences of form and aspect plane _MaxL1The numeric representation color boundaries, and to maximum saturation value C from the form and aspect plane _Maxh1The brightness L that has _Cmaxh1To the pairing color boundaries of brightness sequence of interval of minimum brightness L=0, correct wherein C with the normal linearity interpolation method _MaxL1Non-flat successively decrease or fill up disappearance.

5. method according to claim 1 is characterized in that, said visible light form and aspect plane color equivalent border C _MaxLd, obtain through following method:

5.1 obtain visible light colors visually-perceptible 0 ° of maximum saturation C in space to 359 ° of phase face of all kinds _Maxh2And brightness L _Cmaxh2

5.2, calculate visible light colors space C to 0 ° to 359 ° phase face of all kinds _Maxh2With device color spaces C _Maxh1Ratio value B _Maxh:

B _maxh＝C _maxh2/C _maxh1

Proportionality coefficient F is set _X1With equivalent coefficient F _X2, wherein, F _X1Exemplary value scope 2.50-6.00, F _X2Exemplary value scope 1.50-2.50 calculates B _MaxhStandardization equivalent ratio B _Maxd:

B _MaxhValue is greater than F _X1The standardization of form and aspect plane calculate:

B _Maxd=(B _Maxh-F _X1Maximum scale value-F between)/(proportional band _X1) * 0.1+F _X2

B _MaxhValue is smaller or equal to F _X1Greater than (F _X1The standardization of form and aspect plane-1) is calculated:

B _Maxd=(B _Maxh-(F _X1-1))/(maximum scale value between the proportional band-(F _X1-1)) * 0.05+ (F _X2-0.05)

B _MaxhValue is smaller or equal to (F _X1-1) greater than (F _X1The standardization of form and aspect plane-1.5) is calculated:

B _Maxd=(B _Maxh-(F _X1-1.5))/(maximum scale value between the proportional band-(F _X1-1.5)) * 0.05+ (F _X2-0.1)

B _MaxhValue is smaller or equal to (F _X1-1.5) greater than (F _X1The standardization of form and aspect plane-2) is calculated:

B _Maxd=(B _Maxh-(F _X1-2))/(maximum scale value between the proportional band-(F _X1-2)) * 0.1+ (F _X2-0.2)

B _MaxhValue is smaller or equal to (F _X1-2) greater than (F _X1The standardization of form and aspect plane-2.5) is calculated:

B _Maxd=(B _Maxh-(F _X1-2.5))/(maximum scale value between the proportional band-(F _X1-2.5)) * 0.05+ (F _X2-0.25)

B _MaxhValue equals (F _X1-2.5) standardization that reaches following form and aspect plane is calculated:

B _Maxd=B _MaxhMaximum scale value * 0.05+ (F between/proportional band _X2-0.3);

5.3 calculate the maximum saturation equivalent C of visible light colors space form and aspect plane _Maxhd:

C _maxhd＝C _maxh1×B _maxd

5.4 at abscissa is saturation C, ordinate is the cartesian coordinate plane of brightness L, with C _Maxh2And L _Cmaxh2Value indicates maximum saturation coordinate points D _Maxh2, from a D _Maxh2Connection lengthwise coordinate L=100 point indicates C on line _Maxhd, the brightness sequential value that its ordinate value rounds up is designated as L _Cmaxhd, its coordinate points is designated as D _Maxhd

5.5 indicate the equipment color boundaries of identical form and aspect plane at coordinate plane, indicate D again _MaxhdTo L=0 point line, contain the equipment color boundaries under the prerequisite that its maximum saturation value of guaranteeing corresponding bright sequence in brightness section is smoothly successively decreased, by adjusted D _MaxhdTo L=0 point line and D _MaxhdTo L=100 point line, constitute the equivalent border C of visible light form and aspect plane color _MaxLd

6. method according to claim 5 is characterized in that, 0 ° of maximum saturation C to 359 ° of phase face of all kinds in alleged visible light colors visually-perceptible space in step 5.1 _Maxh2And brightness L _Cmach2, obtain through following method:

6.1 x, y and z numerical value with CIE1931XYZ standard colorimetric observer spectral tristimulus value medium wavelength 380nm to 780nm interval 5nm; And white CIEXYZ tristimulus values of computing equipment color space application; Method so that CIE recommends is calculated L, C and h numerical value; And with the h value merger form and aspect plane that rounds up; Take wherein the L and the C numerical value of form and aspect plane in 38 ° to the 317 ° form and aspect intervals, linear interpolation is calculated the L and the C value of blank form and aspect plane wherein in addition, with L and the C value maximum saturation C as the corresponding form and aspect plane in visible light colors visually-perceptible space _Maxh2And brightness L _Cmach2

6.2 white CIEXYZ tristimulus values of red, green with the CIERGB color space in primary colors canonical parameter and computing equipment color space application calculated and generated CIELAB Space L, C and h numerical value all red, green and the synthetic color of primary colors, comprising:

With CIERGB space RGB three primary colors chromaticity parameter and the white parameter of D65, calculate 3 * 3 matrix coefficients with same procedure in the claim 4:

X _r，max＝0.4108?Y _r，max＝0.1481?Z _r，max＝0

X _g，max＝0.3210?Y _g，max＝0.8401?Z _g，max＝0.0105

X _b，max＝0.2185?Y _b，max＝0.0118?Z _b，max＝1.0783

In the form and aspect plane of merger, select 0 ° to 37 ° and 318 ° to 359 ° form and aspect planes for use, and extract wherein maximum saturation C _Maxh2And brightness L _Cmach2Numerical value.

7. method according to claim 1 is characterized in that, said method comprises that also a plurality of saturation expansion ratios are set regulates and control coefficient B I _KxStep, the number range 0.00-1 of coefficient allows with (BI _K1* BI _L) and (BI _K2* BI _L) as the expansion of the pixel practical application saturation under different specified requirementss ratio.

8. method according to claim 1 is characterized in that, said method also comprises the phase face interval border H that checks colors is set more _DxAnd H _GxStep, 0 °-359 ° of border form and aspect plane number ranges are used with interval outer different saturation and are expanded ratio color in the form and aspect plane interval is set; Form and aspect plane interval border inboard is provided with intermediate color separately, and number range 0-20 regulates and control the interior color saturation expansion ratio of form and aspect interval border and is smoothly transitted into outside the border at interval.

9. method according to claim 1 is characterized in that, said method also comprises a plurality of saturation threshold value C are set _GxStep, saturation threshold value scope 0.00-1, threshold value is used the saturation ratio value boundary of different saturation expansion ratio as same brightness sequence color; In the high-end side of threshold value transition region is set, the saturation ratio value scope 0.00-0.2 of transition region; , when bulletin colour is interval mutually, calculates realization saturation proportion threshold value in form and aspect border transition district and seamlessly transit in the saturation threshold application.

10. color digital image is carried out the system of visually-perceptible high-fidelity conversion, it is characterized in that said system comprises:

(1) the equipment colour vision aware space form and aspect plane color boundaries computing module of display color digital picture comprises:

(1-1) red, the green and primary colors value transform of device color spaces is CIELAB Space L, C and h value computing unit,

(1-2) equipment colour vision aware space form and aspect plane color boundaries extraction unit; Get into corresponding benchmark form and aspect plane with the form and aspect h value merger equipment color that rounds up; Get into the corresponding bright sequence with the merger that rounds up of L value, extract the color maximum saturation value C of each brightness sequence of form and aspect plane _MaxL1, calculate basic numerical value as the color boundaries of this form and aspect plane,

(1-3) form and aspect plane color boundaries C _MaxL1Smooth unit is selected maximum saturation value C in the form and aspect plane _Maxh1The brightness L that has _Cmaxh1The pairing color boundaries C of brightness sequence of interval to minimum brightness L=0 _MaxL1, calculate smooth boundary with the normal linearity interpolation algorithm, remedy C _MaxL1Non-flat is successively decreased or is filled up disappearance, the color boundaries that calculates and by brightness L _Cmaxh1To the corresponding color boundaries of the brightness sequence of interval of L=100, represent the application color boundaries C of this form and aspect plane _MaxL1

(2) visible light colors visually-perceptible space form and aspect plane color equivalent border C _MaxLThe d computing module;

(3) color digital image pixel color pattern forward conversion and merger form and aspect plane and brightness block comprise:

(3-1) color digital image pixel color rgb value is converted into the computing unit of L, C and the h value in CIELAB space, wherein, h is a hue angle, and L is that brightness, C are saturation,

(3-2) pixel color form and aspect plane and brightness sequence merger unit; With the color of image spatial division is 360 reference colours phase face; Get into corresponding benchmark form and aspect plane with the merger that rounds up of form and aspect h value; With brightness L scope division in the form and aspect plane is 101 consensus sequences, gets into the corresponding bright sequence with the merger that rounds up of brightness L value;

(4) image pixel color saturation value expansion module comprises:

(4-1) pixel color saturation expansion ratio BI _LComputing unit:

BI _L＝C _maxLd/C _maxL1

Wherein, C _MaxLdFor pixel belongs to the brightness sequence at the borderline intensity value of visible light form and aspect plane color equivalent, C _MaxL1Be the intensity value of pixel place brightness sequence on equipment form and aspect plane color boundaries,

(4-2) saturation expansion ratio BI _LCharacterization regulation and control computing unit comprises:

(4-2-1) saturation expansion ratio regulation and control coefficient B I is set _Kx, number range 0.00-1,

BI _LY＝BI _L×BI _Kx

Wherein, BI _KxAllow to be provided with a plurality of specified requirementss that are respectively applied for, BI _LYExpand ratio for regulating and control the back saturation,

(4-2-2) the interval border H in pairs of form and aspect plane is set _DxAnd H _Gx, 0 °-359 ° of number ranges, H _DxAnd H _GxAllow to be provided with many to being respectively applied for specified requirements:

Color h>=H _DxAnd h≤H _Gx, BI _LY=BI _L* BI _K2

All the other colors, BI _LY=BI _L* BI _K1

Border inboard form and aspect plane transition region is provided with number range 0-20,

(4-2-3) saturation proportion threshold value C is set _Gx, number range 0.00-1, C _GxAllow to be provided with a plurality of specified requirementss that are respectively applied for:

C _GxBe worth following color, BI _LY=BI _L* BI _K2

C _GxBe worth above color, BI _LY=BI _L* BI _K1

C _GxBe worth a high-end side transition region number range 0.00-0.2 be set,

(4-3) saturation mapping flex point C _GLComputing unit:

C _gL＝C _maxL1×X _S1

Proportionality coefficient X is set _S1Number range 0.65-0.95,

(4-4) pixel color saturation C _LExpand to C _LNComputing unit:

C _LN＝C _L×BI _LY，

If C _LNGreater than C _GLThen calculate mapping:

C _LN＝C _gL+(C _LN-C _gL)/(C _maxLd-C _gL)×(C _maxL1-C _gL)；

(5) image pixel color mode transformation by reciprocal direction and standardization module, said calculating comprise the L of image pixel color, h and the saturation C that obtains through module (4) expansion _LNNumerical computations is standard R, G and B value.

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