CN100556071C - image processing method - Google Patents

Abstract

An image processing method is used to enhance the color saturation of an image. The image includes at least one pixel, the pixel has pixel data, and the pixel data includes data of three colors. The image processing method includes: first, calculating the color purity value of the pixel, the color purity value is the difference between the maximum grayscale value and the minimum grayscale value in the data of the three colors, and obtaining a level factor corresponding to the color purity value according to the color purity value; then, determining the pixel enhancement matrix according to the level factor, and generating adjusted pixel data according to the enhancement matrix and the pixel data.

Description

image processing method

technical field

The present invention relates to an image processing method, and in particular to an image processing method with a color enhancement function.

Background technique

With the development of third-generation (3G) communication technology, many mobile communication devices using 3G multimedia, such as mobile phones or personal digital assistants (PDA), are often used to receive and display digital images data, allowing users to view and play digital images while operating these mobile communication devices. In view of the lack of image color saturation of digital image signal sources, such as charge coupled devices (CCD) or mobile TV signals, how to display more saturated images on the display screens of the aforementioned communication devices has become an issue for many manufacturers. focus of development.

Based on the purpose of optimizing the presentation of the image effect, various image processing methods have been proposed to improve the color saturation of the image. US Patent No. 6771311 discloses an "automatic color saturation enhancement technology" (automatic color saturation enhancement). In this patent, four predictors must be obtained first, and then the level factor is further calculated. Since this method must go through quite complex mathematical operations, it will cost a considerable amount of cost when implemented on a driver IC.

In addition, US Patent No. 6721000 discloses an "adaptive pixel-level color enhancement for a digital camera" (adaptive pixel-level color enhancement for a digital camera). This patent deals with the color elements of the YUV color space (colors pace), and multiplies the U element and the V element by the level factor to achieve the effect of increasing color saturation. However, this method will make the grayscale value of these pixels higher than the highest grayscale value (usually 255) in general color rendering after the color enhancement is performed on the pixels already having high color saturation. In such a situation, these pixels can only be represented with the highest grayscale value, resulting in "image clipping" (clipping), which cannot present the original color levels of these pixels, causing the image to lose its original subtle information.

In the 2004 Society for Information Display (SID) conference, Philips research laboratories (Philips research laboratories) published a paper entitled "Technology for displaying more realistic colors using smaller color gamut mobile devices". colors from small-gamut mobile displays), a post-processing method that alleviates the phenomenon of "image cropping" is proposed. This technology deals with the situation where the grayscale value of the adjusted pixel is greater than the maximum grayscale value and the grayscale value of the pixel is smaller than the minimum grayscale value (for example, 0). It first adds a certain degree of white to the entire image at the same time. , so that the grayscale values of the entire image are all greater than or equal to 0, and then the image is normalized by the maximum value of the pixel, so that the grayscale values of the entire image are all less than or equal to 255. Although this method will not affect the color tone of the image, it will reduce the color saturation of the image.

Contents of the invention

The present invention provides a space-adaptive image processing method. According to the color purity of each pixel in the image, the color enhancement matrix belonging to the pixel is calculated, and then the color of the pixel is adjusted to different degrees. Each pixel in the image has a different degree of color enhancement. A relatively large degree of color enhancement is performed for pixels with a small color purity value, and a relatively small degree of color enhancement is performed for pixels with a large color purity value. It can effectively solve the phenomenon of image cropping without changing the original color tone of the pixels.

The invention provides an image processing method, which is suitable for enhancing the color saturation of an image. The image includes at least one pixel, the pixel has pixel data, and the pixel data includes data of three colors. The image processing method includes: first, calculate the color purity value of the pixel, wherein the color purity value is the difference between the maximum gray scale value and the minimum gray scale value in the data of the three colors, and obtain the corresponding color purity value according to the color purity value The level factor; then, the level factor determines the enhancement matrix of the pixel, and generates adjusted pixel data according to the enhancement matrix and the pixel data.

In order to make the above-mentioned features and advantages of the present invention more comprehensible, preferred embodiments will be described in detail below together with the accompanying drawings.

Description of drawings

FIG. 1 shows a flowchart of an image processing method according to a preferred embodiment of the present invention.

Figure 2 shows the CIE standard chromaticity diagram.

Figure 3A is a color image one.

FIG. 3B is a distribution diagram of chromaticity coordinates of the image in FIG. 3A .

FIG. 4A is an adjusted image of the color image in FIG. 3A after image processing.

FIG. 4B is a distribution diagram of chromaticity coordinates of the image in FIG. 4A.

Figure 5A is the second color image.

FIG. 5B is a distribution diagram of chromaticity coordinates of the image in FIG. 5A.

FIG. 6A is an adjusted image of the color image 2 in FIG. 5A after image processing.

FIG. 6B is a distribution diagram of chromaticity coordinates of the image in FIG. 6A.

Detailed ways

The image processing method proposed by the present invention is suitable for enhancing the color saturation of an image, and the image includes at least one pixel. The pixel has corresponding pixel data, and the pixel data includes data of three colors, and the pixel data corresponding to the three colors includes (C1, C2, C3). If the three colors are red, green and blue, for example, C1 can be set as the grayscale value of the red data, C2 can be set as the grayscale value of the green data, and C3 can be set as the grayscale value of the blue data.

Please refer to FIG. 1 , which shows a flowchart of an image processing method according to a preferred embodiment of the present invention. As shown in Figure 1, the image processing method includes steps 11-13: first, calculate the color purity value of the pixel from the pixel data (C1, C2, C3), and obtain the level factor corresponding to the color purity value according to the color purity value ; Next, determine the enhancement matrix of the pixel by the level factor, and generate adjusted pixel data according to the enhancement matrix and the pixel data.

In step 11, the color purity value of the pixel is calculated from the pixel data of the pixel. In this step, the color data of each pixel in the input image is analyzed to estimate the color purity value of the pixel based on the relationship between the color grayscale values of the pixel, and the pixel data (C1, C2, C3 ) as an example, the color purity value can be obtained according to the following formula:

cp=max(C1,C2,C3)-min(C1,C2,C3),

Among them, cp is the color purity value, max(C1, C2, C 3) is the maximum gray scale value among C1, C2 and C3, and min(C1, C2, C3) is the minimum gray scale value among C1, C2 and C3 value. The color purity value is defined as the difference between the maximum gray scale value and the minimum gray scale value among C1, C2 and C3.

The calculated color purity value of the pixel has multiple levels, that is, the color purity value of each pixel can be divided into different levels according to its size, so the degree of color enhancement of each pixel can be further determined. When the calculated color purity value is larger, it means that the proportion of the pixel tending to a specific color is larger when presented. For example, assuming that the pixel data of a certain pixel is that C1 is equal to 18, C2 is equal to 165, and C3 is equal to 80, then max(C1, C2, C3) is C2, and min(C1, C2, C3) is C1, so cp equals 147. It has been defined above that C1 is the grayscale value of red data, and C2 is the grayscale value of green data, so this pixel tends to display the largest proportion of green. The steps of this image processing method will continue to be described below.

Next, as shown in step 12, the level factor corresponding to the color purity value is obtained according to the color purity value. This level factor is used to determine how much the saturation of the pixel will be enhanced. After the color purity value of the pixel is determined, the level factor of the pixel can be further determined according to the value of cp. Assuming that the color purity value can be divided into n levels, and the value of the level factor is s, preferably, the color purity value of each level corresponds to a value of the level factor. Since most pixels have different color purity values, the degree to which they should be enhanced is also different.

Use Figure 2 as an illustration, which shows the CIE standard chromaticity diagram. The triangular area T in Figure 2 represents the range of all colors that the display can display, and the marked points P ₁ and P ₂ correspond to two different pixels, among which the color purity of point P ₁ is relatively high, with a very obvious green color, The color purity of the point _P2 is relatively small, and it is more inclined to white. Since the pixel at point P ₁ already has a relatively high color purity, there is no need to increase its color saturation significantly, and the level factor of P ₁ can be set to a smaller value at this time; on the contrary, because the pixel at point P ₂ The color purity of the pixel is small, so its color saturation can be greatly increased. At this time, the level factor of P ₂ can be set to a larger value.

The method for obtaining the level factor includes: providing a look-up table for obtaining the level factor according to the color purity value. Preferably, the look-up table includes a plurality of pairs (Gi, Si), the number of pairs is the aforementioned level number n, so that i=1～n, and each pair includes a lower limit value Gi and Corresponding to a factor value Si. The lower limit value Gi is used to determine the level of the color purity value of each pixel, and the corresponding factor value Si is used to be designated as the level factor value of the pixel. The lower limit value Gi is between the maximum gray scale value (usually 255) and the minimum gray scale value (usually 0), and the factor value Si is between 0 and 1.

These number pairs include at least two number pairs, such as a first number pair and a second number pair. The first number pair includes the first lower limit value and the corresponding first factor value, and the second number pair includes the second lower limit. value and the corresponding second factor value. Wherein the first lower limit value is greater than the second lower limit value, and the first factor value is smaller than the second factor value. The feature of the lookup table is that these pairs have the property that the lower limit value decreases and the corresponding factor value increases, that is, the larger the lower limit value, the smaller the corresponding factor value, and the smaller the lower limit value, the corresponding The larger the factor value is. Please refer to the following lookup table I, which mainly divides the color purity value into thirteen levels (n=13):

Query Form I

pair(i) Lower limit (Gi) Factor value (Si) 1 178 0 2 162 0.05

3 146 0.10 4 130 0.15 5 114 0.20 6 98 0.25 7 82 0.30 8 66 0.35 9 50 0.40 10 34 0.45 11 18 0.50 12 8 0.55 13 0 0.60

Here, the pixel data (C1, C2, C3) is equal to (18, 165, 80) as an example for illustration. When the color enhancement operation is performed on this pixel, its color purity must be obtained, and the value cp of the color purity of this pixel is 147 according to the aforementioned definition. Then, the level factor of the pixel is judged by the value cp of the color purity and the look-up table I. Preferably, the first lower limit value G1 is set as the maximum lower limit value, i.e. the 1st number pair (178,0) in the look-up table I, and its steps include:

(a) First, judge whether the color purity value is greater than or equal to the first lower limit value. When the color purity value is greater than or equal to the first lower limit value, the level factor is the first factor value corresponding to the first lower limit value ; When the color purity value is less than the first lower limit value, then enter the next step;

(b) Judging whether the color purity value is greater than or equal to the lower limit value of the next number pair, when the color purity value is greater than or equal to the lower limit value of the next number pair, the level factor is the value corresponding to the next number pair Factor value; if this color purity value is still less than the lower limit value of the next number pair, then enter the next step;

(c) Continue to repeat step (b) until the level factor is obtained.

For example, the value cp of the pixel color purity in this example is 147, which is smaller than the first lower limit value 178, so continue to determine whether the value cp is greater than or equal to the lower limit value of the next number pair. According to the lookup table, the next number pair is the second number pair (162, 0.05). Since the value cp is still less than 162, continue to judge the next number pair (146, 0.10) until the color purity value is greater than or until it is equal to the condition of the lower limit value of the next number pair. In this example, when the third number pair (146, 0.10) is judged, since 147 is greater than 146, the factor value 0.10 of the third number pair can be specified as the value s of this pixel level factor, that is, s is equal to 0.10 .

After determining the level factor of the pixel, as shown in FIG. 1 , proceed to step 13, determine the enhancement matrix of the pixel by the level factor, and generate adjusted pixel data according to the enhancement matrix and pixel data. According to the value s of the input level factor, different degrees of enhancement matrices can be obtained to enhance the saturation of pixels with different color purity. The definition of the enhancement matrix can be as follows:

$\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}\\ <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}\\ <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}\end{array}\right]<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Where E is the enhancement matrix, and s is the level factor value. Assuming that the adjusted pixel data is (C1', C2', C3'), it can be obtained by multiplying the enhancement matrix E with the pixel data (C1, C2, C3) of each pixel, as shown below:

$\left[\begin{array}{c}{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="C"\; filenames="C20061016376500131.png,C20061016376500142.png"\; state="\{\{state\}\}">C</figure-callout></span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; \&prime;</span>}\\ {\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; \&prime;</span>}\\ {\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 3</span>}}^{<span\; class="notranslate">\; \&prime;</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\\ \mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 3</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}\\ <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}\\ <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}\end{array}\right]<span\; class="notranslate">\; \&Center\; Dot;</span>\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\\ \mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 3</span>}\end{array}\right]<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

From formula (2), C1', C2', and C3' can be obtained as:

C1'＝(1+s)C1+(-s/2)C2+(-s/2)C3 ......(3.1)

C2'＝(-s/2)C1+(1+s)C2+(-s/2)C3 ......(3.2)

C3'＝(-s/2)C1+(-s/2)C2+(1+s)C3...(3.3)

After the aforementioned original pixel data (C1, C2, C3) are equal to (18, 165, 80), and its level factor is 0.10, after entering the formulas (3.1), (3.2) and (3.3), the obtained C1' is approximately equal to 8. C2' is approximately equal to 177, and C3' is approximately equal to 79. Compared with the original pixel data, it can be observed that the gray scale value C2' of the adjusted green data increases while the gray scale values of the other two colors C1' and C3' is decreased, representing an increase in the proportion of green color compared to the original pixel.

Although the proportion of each color will be adjusted, the color of the image processed by this algorithm can still maintain the hue of the original color, as illustrated below with an example. Assuming that the relationship between the color grayscale values of the red, green and blue data of this pixel before adjustment is C1>C2>C3, and the original hue of the pixel is H, the red, green and blue data of the pixel after the algorithm processing The relationship is C1'>C2'>C3', and the hue of the adjusted pixel is set to H', where:

Among them, the formulas (4.1), (4.2) define the relationship formulas for the hue (Hue) in the known HSV color space (colors pace), and then bring the formulas (3.1), (3.2) and (3.3) into the formula ( 4.2):

From the above calculation results, it can be confirmed that the color tone of the pixel remains unchanged before and after adjustment.

As shown in FIG. 1 , the image processing method further includes step 14: outputting an adjusted image according to the adjusted pixel data. Please refer to Figures 3A-3B, 4A-4B, Figure 3A is the first color image, Figure 3B is the chromaticity coordinate distribution diagram of the image in Figure 3A, Figure 4A is the adjusted image of the color image in Figure 3A after image processing, and Figure 4B is Chromaticity coordinate distribution diagram of the image in Figure 4A. Please observe FIGS. 3B and 4B , after the image processing, the coordinates of each point of the pixel of the color image 1 expand outward in the chromaticity distribution range, which means that the color saturation of different pixels increases.

Please refer to Fig. 5A～5B, 6A～6B again, Fig. 5A is the color image 2, Fig. 5B is the chromaticity coordinate distribution map of Fig. 5A image, Fig. 6A is the adjusted image after the image processing of Fig. 5A color image 2, Fig. 6B is a chromaticity coordinate distribution diagram of the image in FIG. 6A. Please observe FIGS. 5B and 6B at the same time. It can be found that the coordinates of each pixel point of the color image 2 after the image processing also obviously move to the boundary of the distribution range in the chromaticity distribution range. 3A-6B can prove that this image processing method does have the effect of enhancing color saturation and color enhancement.

Since this method uses a single pixel as a processing unit and does not require additional frame memory, it is especially suitable for the low-cost requirements of portable display devices, such as mobile communication devices or PDAs.

Compared with the patent case "Automatic Color Saturation Enhancement Technology" of US Patent No. 6771311, the present invention only needs to process a single pixel, and does not need to analyze the entire image at the same time, which can greatly reduce the computational complexity to effectively reduce the complexity of hardware implementation.

In addition, compared with U.S. Patent No. 6,721,000 "Adaptive Pixel Color Enhancement Technology for Digital Cameras", the method of the present invention is to enhance the color of pixels with relatively low color purity to a greater extent. A smaller degree of color enhancement is performed on pixels with greater color purity, thereby reducing image clipping.

Compared with Philips' paper "A technology for displaying more realistic colors using a mobile device with a smaller color gamut" published at SID2004, the present invention does not need to calculate the maximum and minimum values for the entire image and perform complex calculations to effectively enhance the color of the image. Color saturation can also reduce the complexity of hardware implementation.

The image processing method disclosed in the above embodiments of the present invention can process a single pixel in the image to enhance the color saturation of the image. By first inputting the pixel data of a single pixel to obtain the color purity value of this pixel, and then judging the level factor of this pixel according to the size of the color purity value, and calculating the enhancement matrix belonging to this pixel, and then further by pixel data and The enhancement matrix dynamically adjusts the color value of this pixel. This method effectively solves the phenomenon of image trimming and achieves the purpose of displaying better colors.

To sum up, although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. Those skilled in the art of the present invention can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be defined by the appended claims.

Claims (11)

1. An image processing method, suitable for enhancing the color saturation of an image, the image comprising at least one pixel, the pixel having pixel data, the pixel data comprising data of three colors, the image processing method comprising: (a) Calculate the color purity value of the pixel, which is the difference between the maximum grayscale value and the minimum grayscale value in the data of the three colors, and obtain the color corresponding to the color purity value according to the color purity value The level factor of ; and (b) Determine a 3×3 enhancement matrix of the pixel according to the level factor, and generate adjusted pixel data according to the enhancement matrix and the pixel data. 2. The image processing method according to claim 1, wherein the color purity value has a plurality of levels. 3. The image processing method according to claim 1, wherein the step (a) further comprises: (a1) providing a lookup table for obtaining the level factor according to the color purity value. 4. The image processing method according to claim 3, wherein the look-up table includes a plurality of number pairs, and each of the plurality of number pairs includes a lower limit value and a corresponding factor value. 5. The image processing method according to claim 4, wherein said plurality of pairs comprises at least a first pair and a second pair, the first pair includes a first lower limit value and a first factor value, the The second pair includes a second lower limit value and a second factor value, the first lower limit value is greater than the second lower limit value, and the first factor value is smaller than the second factor value. 6. The image processing method according to claim 5, wherein the step (a1) further comprises: (a11) Determine whether the color purity value is greater than or equal to the first lower limit value, and when the color purity value is greater than or equal to the first lower limit value, the level factor is the first factor value. 7. The image processing method according to claim 6, wherein when the color purity value is less than the first lower limit value, the step (a1) further comprises: (a12) Judging whether the color purity value is greater than or equal to the lower limit value of the next number pair, when the color purity value is greater than or equal to the lower limit value of the next number pair, the level factor is the value of the next number pair factor values; and (a13) When the color purity value is less than the next lower limit value, repeat step (a12) until the level factor is obtained. 8. The image processing method according to claim 1, wherein the three colors are red, green and blue respectively. 9. The image processing method according to claim 1, wherein the adjusted pixel data includes data of another three colors, the gray scale values of the three colors are respectively C1, C2 and C3, and the gray values of the other three colors are The order values are C1', C2' and C3' respectively, and the factor value of this level is s. According to the enhancement matrix: C1'=(1+s)×C1+(-s/2)×C2+(-s/2)×C3, C2'=(-s/2)*C1+(1+s)*C2+(-s/2)*C3, and C3'=(-s/2)×C1+(-s/2)×C2+(1+s)×C3. 10. The image processing method according to claim 9, wherein the other three colors are red, green and blue respectively. 11. The image processing method according to claim 1, further comprising: An adjusted image is output according to the adjusted pixel data.

Images