CN100556071C - Image processing method - Google Patents
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- CN100556071C CN100556071C CNB2006101637652A CN200610163765A CN100556071C CN 100556071 C CN100556071 C CN 100556071C CN B2006101637652 A CNB2006101637652 A CN B2006101637652A CN 200610163765 A CN200610163765 A CN 200610163765A CN 100556071 C CN100556071 C CN 100556071C
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Abstract
A kind of image processing method is applicable to the color saturation that strengthens image.Image comprises at least one pixel, and pixel has pixel data, and pixel data comprises the data of three colors.Image processing method comprises: at first, the color purity value of calculating pixel, color purity value are the maximum gray value in the data of three colors and the difference of minimum gray value, and obtain the rank factor corresponding with the color purity value according to the color purity value; Then, by the gorgeous matrix that increases of rank factor decision pixel, and according to increasing gorgeous matrix and pixel data generation adjustment back pixel data.
Description
Technical field
The invention relates to a kind of image processing method, and particularly increase the image processing method of gorgeous function relevant for a kind of tool color.
Background technology
At the third generation (third-generation, 3G) under the development of mechanics of communication, the multimedia device for mobile communication of many utilization 3G, mobile phone or personal digital assistant (personal digital assistant for example, PDA) etc., often be used to receive and show DID, allow the user in these device for mobile communication of operation, also can view and admire dialling of digital picture and put.Because data image signal source, charge coupled cell (charge couple device for example, CCD) or the color saturation of image deficiency of mobile television signal, how on the display screen of aforementioned communication device, to demonstrate the emphasis that the more saturated image of color has become the development of many manufacturers.
Based on the purpose that the image effect optimization is presented, existing at present multiple image processing method is suggested in order to improve the color saturation of image.The patent case that No. the 6771311st, U.S. Patent number has disclosed a kind of " auto color saturation enhancement techniques " (automatic color saturation enhancement).In this patent, must obtain four pre-operators (predictor) earlier and further calculate the rank factor again.Because the method must be passed through suitable complex mathematical computing, in the time of on being implemented in drive IC, need expend sizable cost.
In addition, the patent case of No. the 6721000th, U.S. Patent number has disclosed a kind of " the adaptability pixel color enhancement techniques that is used for digital camera " (adaptive pixel-level color enhancementfor a digital camera).This patent is that the color element at YUV color space (colors pace) deals with, and U element and V element are multiplied by the rank factor to reach the effect that increases color saturation.Yet, the method do at the original pixel that has just had high color saturation color increase gorgeous after, can make the GTG value of these pixels be higher than general color and be current high gray value (normally 255).Under this kind situation, only can show these pixels, thereby produce " image trimming " phenomenon (clipping), can't present the original gradation of these pixels, make image lose originally trickleer information with high gray value.
At information display conferences in 2004 (society for information display, SID) in, research laboratory, Philip (Philips research laboratories) has delivered the paper of one piece of " using less colour gamut mobile device to show the more technology of color with fidelity " (more realistic colors fromsmall-gamut mobile displays) by name, has proposed a post-processing approach that can alleviate " image trimming " phenomenon.This technology deals with greater than the GTG value of maximum gray value and the pixel situation less than minimum gray value (for example being 0) at the GTG value of adjusting the back pixel, be earlier whole image to be added to a certain degree white simultaneously, the GTG value that makes full images is all more than or equal to 0, again with the maximum of pixel to image normalization, can make the GTG value of full images all be less than or equal to 255.Though the method is unlikely to influence the tone of image, the color saturation of image is descended.
Summary of the invention
The present invention is at the image processing method that a kind of tool space adaptability is provided, and is to go to calculate according to the color purity of each pixel in the image to belong to the gorgeous matrix of increasing of this pixel, and then the color of this pixel is done in various degree adjustment.Each pixel in the image increase gorgeous degree difference, increase gorgeous at the make comparisons color of big degree of the less pixel of color purity value, the color of doing smaller degree at the bigger pixel of color purity value increases gorgeous, not only can solve the image trimming phenomenon effectively, and can not change the original tone of pixel.
The present invention proposes a kind of image processing method, is applicable to the color saturation that strengthens image.Image comprises at least one pixel, and pixel has pixel data, and pixel data comprises the data of three colors.Image processing method comprises: at first, the color purity value of calculating pixel, wherein the color purity value is the maximum gray value in the data of three colors and the difference of minimum gray value, and obtains the rank factor corresponding with the color purity value according to the color purity value; Then, by the gorgeous matrix that increases of rank factor decision pixel, and according to increasing gorgeous matrix and pixel data generation adjustment back pixel data.
For above-mentioned feature of the present invention and advantage can be become apparent, preferred embodiment cited below particularly, and cooperate appended graphicly, be described in detail below.
Description of drawings
It is flow chart according to the image processing method of preferred embodiment of the present invention that Fig. 1 illustrates.
It is the CIE standard chromatic diagram that Fig. 2 illustrates.
Fig. 3 A is a coloured image one.
Fig. 3 B is the chromaticity coordinate distribution map of Fig. 3 A image.
Fig. 4 A is the adjustment image of coloured image after image processing of Fig. 3 A.
Fig. 4 B is the chromaticity coordinate distribution map of Fig. 4 A image.
Fig. 5 A is a coloured image two.
Fig. 5 B is the chromaticity coordinate distribution map of Fig. 5 A image.
Fig. 6 A is the adjustment image of coloured image two after image processing of Fig. 5 A.
Fig. 6 B is the chromaticity coordinate distribution map of Fig. 6 A image.
Embodiment
Image processing method proposed by the invention is to be applicable to the color saturation that strengthens image, and this image comprises at least one pixel.This pixel has corresponding pixel data, and pixel data then comprises the data of three colors, corresponding to the pixel data of three colors comprise (C1, C2, C3).Three colors for example are red, green and blue, then C1 can be set at the GTG value of red data, and C2 is set at the GTG value of green data, and C3 is set at the GTG value of blue data.
Please refer to Fig. 1, it illustrates is flow chart according to the image processing method of preferred embodiment of the present invention.As shown in Figure 1, image processing method comprises step 11～13: at first, (C1, C2 C3) go the color purity value of calculating pixel, and obtain the rank factor of color purity value correspondence therewith according to the color purity value by pixel data; Then, determine the gorgeous matrix of increasing of this pixel, and increase gorgeous matrix and pixel data generation adjustment back pixel data according to this by the rank factor.
In step 11, be the color purity value of calculating this pixel by the pixel data of pixel.In this step, the color data of each pixel in the input picture is analyzed, with the relation between the color gray values of pixel by this, estimate the color purity value of this pixel, with pixel data (C1, C2, C3) be example, the color purity value can obtain according to following formula:
cp＝max(C1，C2，C3)-min(C1，C2，C3)，
Wherein, cp is the color purity value, and max (C 3 for C1, C2) is the maximum gray value among C1, C2 and the C3, and min (C1, C2 C3) are minimum gray value among C1, C2 and the C3.The color purity value is defined as the maximum gray value among C1, C2 and the C3 and the difference of minimum gray value.
The color purity value of the pixel of being calculated has a plurality of ranks, that is the color purity value of each pixel is divided into different ranks according to its big I, thereby can determine further that each pixel will increase gorgeous degree.When the color purity value that is calculated is big more, represent this pixel big more in being the ratio of tending to specific color now.For instance, the pixel data of supposing certain pixel is that C1 equals 18, and C2 equals 165, and C3 equals 80, then max (C1, C2 C3) are C2, and min (C1, C2 C3) are C1, so cp equals 147.Aforementionedly defined the GTG value that C1 is a red data, and C2 is the GTG value of green data, so this pixel tends to present green ratio maximum.Below go on to say the step of this image processing method.
Then, shown in step 12, be worth the corresponding rank factor to obtain with color purity according to the color purity value.This rank factor is with deciding the saturation of pixel with the degree that is enhanced.After the color purity value of pixel has determined, determine the rank factor of this pixel further according to the big I of cp value.Suppose that the color purity value can be divided into n rank, and the numerical value of the rank factor being s, preferably, is the numerical value that makes the corresponding rank factor of other color purity value of each grade.Because the color purity value of most of pixel is not quite similar, its this degree that is enhanced of institute is difference also.
Do explanation with Fig. 2, it illustrates is the CIE standard chromatic diagram.Delta-shaped region T among Fig. 2 represents the scope of all colors that display can show, the some P of sign
1And P
2Be indivedual corresponding two different pixels, its mid point P
1Color purity bigger, have very significantly green, and some P
2Color purity less, it is partial to white.Owing to put P
1Pixel had bigger color purity, therefore need not significantly increase its color saturation again, this moment can be with P
1The rank factor be set at smaller value; On the contrary, owing to put P
2Pixel color purity little, therefore can increase its color saturation greatly, this moment can be with P
2The rank factor be set at higher value.
The method that obtains the rank factor comprises: question blank is provided, in order to according to the color purity value to obtain the rank factor.Preferably, this question blank comprise a plurality of several to (Gi, Si), several right numbers promptly are aforesaid number of levels n, make i=1～n, each is several to respectively comprising a lower limit Gi and a corresponding factor values Si.Lower limit Gi is in order to the rank of the color purity value of differentiating each pixel, and corresponding factor values Si is then in order to be designated as the rank factor values of pixel.Wherein lower limit Gi is between maximum gray value (being generally 255) and minimum gray value (being generally 0), and factor values Si is then between 0 and 1.
These are several right to comprising two numbers at least, for example be first several to and second several right, first is several to comprising first lower limit and corresponding factor I value, and second is several to comprising second lower limit and corresponding factor value.Wherein first lower limit is greater than second lower limit, and the factor I value is less than the factor value.The characteristics of question blank are, these are several to having the character that lower limit is successively decreased and corresponding factor values increases progressively, that is when lower limit is big more, its corresponding factor values is more little, and lower limit is got over hour, and its corresponding factor values is big more.Please refer to following question blank I, mainly the color purity value divided into 13 ranks (n=13):
Question blank I
| Several to (i) | Lower limit (Gi) | Factor values (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 |
At this, similarly (C1, C2 C3) equal (18,165,80) and do explanation for example with pixel data.When this pixel is done color and increased gorgeous operation, must obtain its color purity, the numerical value cp that is obtained this pixel color purity by aforesaid definition is 147.Then, the rank factor of going to differentiate this pixel of numerical value cp and the question blank I by color purity.Preferably, be that the first lower limit G1 is set at the greatest lower bound value, promptly the 1st number among the question blank I is to (178,0), and its step comprises:
(a) at first, whether judge this color purity value more than or equal to first lower limit, when this color purity value during more than or equal to first lower limit, then the rank factor is the factor I value of the first lower limit correspondence; When color purity value during, then enter next step less than first lower limit;
(b) whether judge this color purity value more than or equal to the right lower limit of next number, when this color purity value during more than or equal to the right lower limit of next number, the rank factor for this reason next number to pairing factor values; If this color purity value still less than the right lower limit of next number, then enters next step;
(c) continue repeating step (b), up to obtaining the rank factor.
For instance, the numerical value cp of the pixel color purity of this example is 147, and therefore it continue to judge that whether numerical value cp is more than or equal to the right lower limit of next number less than first lower limit 178.According to question blank, next number is to being that the 2nd number is to (162,0.05), because numerical value cp is still less than 162, therefore continue (146,0.10) to be judged, till satisfying the condition of this color purity value more than or equal to the right lower limit of next number to next number.In this example, when determining the 3rd number to (146,0.10), because 147 greater than 146, factor values 0.10 that therefore can the 3rd number is right is specified the numerical value s of the pixel scale factor for this reason, and promptly s equals 0.10.
After the rank factor that has determined pixel, as shown in Figure 1, enter step 13, by the gorgeous matrix that increases of rank factor decision pixel, and according to increasing gorgeous matrix and pixel data generation adjustment back pixel data.Numerical value s according to the rank factor of importing can obtain the gorgeous matrix that increases in various degree, has the saturation of the pixel of different color purity in order to enhancing.The definition that increases gorgeous matrix can be as follows:
Wherein E increases gorgeous matrix, and s is the rank factor values.Suppose that adjusting the back pixel data is (C1 ', C2 ', C3 '), the pixel data that it can be by will increasing gorgeous matrix E and each pixel (C1, C2 C3) do matrix multiple and get, and be as follows:
By formula (2) can obtain C1 ', C2 ', C3 ' is respectively:
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)
(C1, C2 C3) equal (18 with aforementioned original pixel data, 165,80), its rank factor is 0.10 to bring formula (3.1), (3.2) and (3.3) into afterwards, and the C1 ' that obtains approximates 8, C2 ' approximates 177, C3 ' approximates 79, and can be observed after raw pixel data is compared, and this GTG value C2 ' that adjusts the back green data strengthens and the GTG value C1 ' and the C3 ' of other two kinds of colors reduce, representing compared to original pixels, green color presents ratio to be increased.
Though the shared ratio regular meeting process of each color is adjusted, and still can keep the tone of color originally through the image color after this algorithm process, below with an example explanation.The pass of supposing this pixel color gray values of redness, green and blue data before adjustment is C1＞C2＞C3, and the original tone of pixel is H, pixel is C1 '＞C2 '＞C3 ' in the pass via the redness after the algorithm process, green and blue data, and the tone of adjusted pixel is decided to be H ', wherein:
Wherein formula (4.1), (4.2) are tone (Hue) definitional relation in the known HSV color space (colors pace), formula (3.1), (3.2) are taken in the formula (4.2) with (3.3) again:
It is constant to confirm the tone of pixel before and after adjusting by above result of calculation.
As shown in Figure 1, image processing method also comprises step 14: adjust the back image according to adjusting back pixel data output.Please refer to Fig. 3 A～3B, 4A～4B, Fig. 3 A is a coloured image one, and Fig. 3 B is the chromaticity coordinate distribution map of Fig. 3 A image, and Fig. 4 A is the adjustment image of coloured image after image processing of Fig. 3 A, and Fig. 4 B is the chromaticity coordinate distribution map of Fig. 4 A image.Please observe Fig. 3 B and 4B, after image processing, each point coordinates of the pixel of coloured image one expands outwardly in the colourity distribution, represents the color saturation of different pixels to increase.
Please refer to Fig. 5 A～5B, 6A～6B again, Fig. 5 A is a coloured image two, and Fig. 5 B is the chromaticity coordinate distribution map of Fig. 5 A image, and Fig. 6 A is the adjustment image of coloured image two after image processing of Fig. 5 A, and Fig. 6 B is the chromaticity coordinate distribution map of Fig. 6 A image.Please observe Fig. 5 B and 6B simultaneously, can find that the coordinate of the pixel each point of the coloured image two after the process image processing also clearly moves to the distribution border in the colourity distribution.Really have by provable this image processing method of Fig. 3 A～6B and to strengthen color saturation and color increases gorgeous effect.
Since the method be with single pixel as processing unit, need not increase extra video memory (frame memory), be particularly suitable for portable display device, for example be the low-cost requirement of device for mobile communication or PDA.
With the patent case " auto color saturation enhancement techniques " of No. the 6771311st, U.S. Patent number in comparison, the present invention only needs to process at single pixel, needn't perform an analysis to whole image simultaneously, can significantly reduce computational complexity to reduce hard-wired complexity effectively.
In addition, compare with the patent case " the adaptability pixel color enhancement techniques that is used for digital camera " of No. the 6721000th, U.S. Patent number, way of the present invention is to increase gorgeous to the color that the less pixel of color purity compares big degree, the color that the bigger pixel of color purity is compared little degree increases gorgeous, therefore can alleviate image trimming (clipping) phenomenon.
" use the mobile device of less colour gamut to show the more technology of color with fidelity " relatively with the Philip in the paper that SID2004 delivered, the present invention needn't and carry out the color saturation that complex calculations can effectively strengthen image at whole image maximizing and minimum value, also can reduce hard-wired complexity.
The disclosed image processing method of the above embodiment of the present invention can deal with at single pixel in the image, to strengthen the color saturation of image.By importing the pixel data of single pixel earlier to ask for the color purity value of this pixel, judge the rank factor of this pixel again according to the size of this color purity value, belong to the gorgeous matrix of increasing of this pixel and calculate, again and then by pixel data and increase gorgeous matrix the color value of this pixel is done dynamically to adjust.This mode has solved the phenomenon of image trimming effectively, and reaches and show the more purpose of high-quality color.
In sum, though the present invention discloses as above with preferred embodiment, so it is not in order to limit the present invention.The persond having ordinary knowledge in the technical field of the present invention, without departing from the spirit and scope of the present invention, when being used for a variety of modifications and variations.Therefore, protection scope of the present invention is as the criterion when looking appended the claim scope person of defining.
Claims (11)
1. an image processing method is applicable to the color saturation that strengthens image, and this image comprises at least one pixel, and this pixel has pixel data, and this pixel data comprises the data of three colors, and this image processing method comprises:
(a) calculate the color purity value of this pixel, the maximum gray value in the data that this color purity value is these three colors and the difference of minimum gray value, and obtain the rank factor corresponding with this color purity value according to this color purity value; And
(b) determine 3 * 3 of this pixel to increase gorgeous matrix by this rank factor, and increase gorgeous matrix and this pixel data generation adjustment back pixel data according to this.
2. image processing method according to claim 1, wherein this color purity value has a plurality of ranks.
3. image processing method according to claim 1, wherein this step (a) also comprises:
(a1) provide question blank, in order to according to this color purity value to obtain this rank factor.
4. image processing method according to claim 3, wherein this question blank comprise a plurality of several right, described a plurality of several to respectively comprising lower limit and corresponding factor values.
5. image processing method according to claim 4, wherein said a plurality of several to comprising that at least first is several several right to reaching second, these are first several to comprising first lower limit and factor I value, these are second several to comprising second lower limit and factor value, this first lower limit is greater than this second lower limit, and this factor I value is less than this factor value.
6. image processing method according to claim 5, wherein this step (a1) also comprises:
(a11) whether judge this color purity value more than or equal to this first lower limit, when this color purity value during more than or equal to this first lower limit, this rank factor is this factor I value.
7. image processing method according to claim 6, wherein when this color purity value during less than this first lower limit, this step (a1) also comprises:
(a12) whether judge this color purity value more than or equal to the right lower limit of next number, when this color purity value during more than or equal to the right lower limit of this next number, this rank factor is the right factor values of this next number; And
(a13) when this color purity value during less than this next one lower limit, repeating step (a12) is to obtaining this rank factor.
8. image processing method according to claim 1, wherein these three colors are respectively red, green and blue.
9. image processing method according to claim 1, wherein this adjustment back pixel data comprises the data of three colors in addition, the GTG value of these three colors is respectively C1, C2 and C3, the GTG value of three colors is respectively C1 ', C2 ' and C3 ' in addition, this rank factor values is s, increases gorgeous matrix according to this:
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. image processing method according to claim 9, wherein three colors are respectively red, green and blue in addition.
11. image processing method according to claim 1 also comprises:
Export adjustment back image according to this according to this adjustment back pixel count.
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| CN102045575B (en) * | 2009-10-21 | 2014-03-12 | 英属开曼群岛商恒景科技股份有限公司 | Pixel color correction method and pixel color correction device |
| CN102752605A (en) * | 2011-09-30 | 2012-10-24 | 新奥特（北京）视频技术有限公司 | Method and system for regulating image/video color saturation degree |
| CN104732952B (en) * | 2013-12-23 | 2017-08-11 | 昆山国显光电有限公司 | Color displays processing method and system |
| CN105304062B (en) * | 2015-11-18 | 2018-01-26 | 合一智能科技（深圳）有限公司 | The colourity adjusting method and device of LCDs |
| CN106296751A (en) * | 2016-08-16 | 2017-01-04 | 中国石油化工股份有限公司 | The color processing method that a kind of view-based access control model is experienced |
| CN108124139A (en) * | 2016-11-28 | 2018-06-05 | 深圳市中兴微电子技术有限公司 | A kind of pixel processing method and processing device |
| CN106657847B (en) * | 2016-12-14 | 2019-08-13 | 广州视源电子科技股份有限公司 | Color saturation of image method of adjustment and system |
| CN108364620B (en) * | 2018-03-30 | 2019-07-12 | 惠科股份有限公司 | The driving method of liquid crystal display device |
| CN113556525B (en) * | 2020-04-23 | 2023-08-15 | 深圳市万普拉斯科技有限公司 | Color temperature adjusting method, device, computer equipment and storage medium |
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