Selective self-adaptive low-power-consumption image display method

Abstract

The invention is applicable to the technical field of image display, and particularly relates to a selective self-adaptive low-power-consumption image display method, which comprises the following steps: S100: acquiring an input image in a png format; s200, editing a division rule, and based on the division rule, clustering the input image into a class A, a class B and a class C; the division rule is that an average gray scale of three channels in the input image is calculated by using an average gray scale calculation formula, a gray scale threshold is configured, and if the average gray scale of the input image is smaller than the gray scale threshold, the input image is classified into a class A; and drawing a three-channel histogram by taking the gray value corresponding to the 8-bit digital image as an abscissa and the pixel number corresponding to each gray value as an ordinate, and defining a high-gray stage. According to the method, the input images are divided into the class A, the class B and the class C by using the overall brightness of the images, and adaptive processing is performed, so that the contradiction between the visual effect and the display power consumption is relieved.

Description

Technical Field

[0001] The present invention relates to the field of image display technology, and in particular to a selective adaptive low-power image display method. Background Art

[0002] In display devices, the display panel consumes most of the battery power, which requires frequent charging of display products and reduces the user experience; however, current low-power technology makes it difficult to strike a balance between image visual effects and display power consumption.

[0003] The current low-power image processing technology processes all input images indiscriminately, that is, regardless of the characteristics of the input images, the same low-power processing algorithm is used. However, some input images have extremely low brightness, so there is no need to reduce their display power consumption. Some input images have serious imbalance in the three channels in the highlight area, and the use of a constant low-power algorithm will cause color distortion. In other words, a single low-power solution is not suitable for all images. Therefore, "how to use an adaptive histogram to correct the image after reducing power consumption" is the technical problem that the present invention needs to solve. Summary of the invention

[0004] The object of the present invention is to provide a selective adaptive low-power image display method to solve the problem of "how to use an adaptive histogram to correct an image after reducing power consumption" mentioned in the background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A selective adaptive low-power image display method, the method comprising:

[0007] S100: Acquire an input image, wherein the input image is in png format;

[0008] S200: Editing a classification rule, and clustering the input image into three categories, namely, category A, category B, and category C, based on the classification rule;

[0009] The division rules are:

[0010] The average grayscale of the three channels in the input image is calculated by using the average grayscale calculation formula, and a grayscale threshold is configured. If the average grayscale of the input image is less than the grayscale threshold, the input image is classified into category A;

[0011] With the grayscale value corresponding to the 8-bit digital image as the horizontal coordinate and the number of pixels corresponding to each grayscale value as the vertical coordinate, a three-channel histogram is drawn to define the high-gray stage, traverse the maximum proportion of the high-gray stage in the three-channel histogram, and calculate the overlapping area of ​​the high-gray stage in the three-channel histogram, and define the input image with the maximum proportion of the high-gray stage greater than 0.025 or the overlapping area less than 0.015 as Class B;

[0012] The input images other than those in categories A and B are defined as category C;

[0013] S300: receiving an image to be processed input by a user, and determining whether the image to be processed belongs to category A.

[0014] If yes, directly output the image to be processed; if no, continue to determine whether the image to be processed belongs to category B;

[0015] If yes, calculate the pixel grayscale of each pixel, establish the corresponding relationship between the pixel grayscale and the reduction coefficient, reduce the pixel grayscale based on the corresponding relationship, process the reduced image to be processed using a preset low-power scheme, and output it; if no, continue to determine whether the image to be processed belongs to category C;

[0016] If so, a correction factor is selected, the correction factor and the histogram correction formula are integrated, the three-channel histogram is corrected to obtain an intermediate image, the image to be processed is processed using a histogram matching algorithm, and the intermediate image is used for deviation to obtain a result image and output it.

[0017] Furthermore, the average grayscale calculation formula in S200 is:

[0018]

[0019] Where GLav is the average grayscale, W and H are the horizontal and vertical resolutions of the input image, respectively, and R(i,j), G(i,j) and B(i,j) are the grayscales of the pixels in the i-th row and j-th column in the red, green and blue channels, respectively.

[0020] Furthermore, the step of “configuring a grayscale threshold, and if the average grayscale of the input image is less than the grayscale threshold, classifying the input image into category A” includes:

[0021] The grayscale threshold is marked as 80, denoted as GLth=80;

[0022] If GLav﹤GLth=80 of the input image, the input image is classified into class A.

[0023] Furthermore, the step of "defining a high gray stage" includes:

[0024] Traverse the maximum grayscale value in the three-channel histogram and record it as GLmax;

[0025] The portion of the three-channel histogram whose grayscale value is within the range of 0.8×GLmax to GLmax is defined as a high gray stage.

[0026] Furthermore, the step of “traversing to find the maximum proportion of the high gray stage in the three-channel histogram” includes:

[0027] The maximum proportion formula is used to calculate the maximum proportion of the high gray stage in the three-channel histogram, which is recorded as kh;

[0028] The maximum proportion formula is:

[0029]

[0030] Furthermore, the step of “calculating the overlapping area of ​​the high gray stage in the three-channel histogram” includes:

[0031] If the input image does not belong to category A, the overlapping area calculation formula is used to calculate the overlapping area of ​​the high gray stage in the three-channel histogram, which is recorded as OAh;

[0032] The overlapping area calculation formula is:

[0033]

[0034] Furthermore, the step of “drawing a three-channel histogram with the grayscale value corresponding to the 8-bit digital image as the horizontal coordinate and the number of pixels corresponding to each grayscale value as the vertical coordinate” includes:

[0035] In each channel, the histogram statistical formula is used to calculate the number of pixels corresponding to each gray value in turn, and then integrated to generate a three-channel histogram;

[0036] The histogram statistical formula is:

[0037] HkD i =card{p|gl r =i},i=0,1,...,255

[0038] HkD i =card{p|gl g =i},i=0,1,...,255

[0039] HkDJ i =card{p|gl b =i},i=0,1,...,255

[0040] Among them hR iIndicates the number of pixels with grayscale value i in the red channel of the input image, where i is an integer in the range of 0 to 255, p is a pixel of the input image, and gl r is the grayscale value of the red channel of pixel p.

[0041] Furthermore, the step of “limiting the pixel grayscale” includes:

[0042] The pixel grayscale is narrowed by using the narrowing coefficient, wherein the range of the narrowing coefficient is 0-1.

[0043] Furthermore, the step of "selecting a correction factor, integrating the correction factor and the histogram correction formula, correcting the three-channel histogram to obtain an intermediate image, applying a histogram matching algorithm to process the image to be processed, deviating the intermediate image, obtaining a result image, and outputting it" includes:

[0044] Based on the average grayscale, a correction factor is selected and recorded as k;

[0045] The selection process is:

[0046]

[0047] Substituting k into the adaptive histogram correction formula, and correcting the three-channel histogram;

[0048] The adaptive histogram correction formula is:

[0049]

[0050] DC 1 =(C max -C min )×0.1875;

[0051] DC 2 =(C max -C min )×0.40625;

[0052] C start =C min +DC 1 ;

[0053] Wherein C represents the color channel of the image to be processed, and C is R, G or B; histC i is the histogram of channel C, histC i ' is the histogram of channel C after correction, is the histogram of channel C after offset, i is the grayscale value, 0≤i≤255, Cmax is the maximum grayscale value of channel C, DC 1 is the first correction distance, DC2 is the second corrected distance, C start is the minimum grayscale value after correction.

[0054] Furthermore, the step of "applying a histogram matching algorithm to process the image to be processed, and using the intermediate image for deviation to obtain a result image, and outputting it" includes:

[0055] The image to be processed is shifted toward the intermediate image by using a histogram matching algorithm in the prior art, and a result image is generated.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] By using the overall brightness of the image, the input image is divided into Class A, Class B and Class C; among them, the brightness of the Class A image is extremely low and no power reduction processing is performed; the histograms of the three channels of the Class B image are seriously unbalanced in the high-gray stage, or the overlapping area of ​​the histograms of the three channels is very small, or the number of pixels in a certain channel is extremely large, and the low-power method based on color space conversion in the existing technology is used to reduce power consumption; the three channels of the Class C image are not seriously unbalanced in the high-gray stage, and an adaptive histogram can be used to correct the image after power consumption reduction to ensure that the image maintains a considerable image visual effect while reducing power consumption, which greatly alleviates the contradiction between visual effect and display power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flowchart of a selective adaptive low-power image display method provided by an embodiment of the present invention;

[0059] Figure 2 A schematic diagram of adaptive histogram correction of the selective adaptive low-power image display method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] In Example 1, Figure 1 The implementation process of the selective adaptive low-power image display method provided by the embodiment of the present invention is shown, and is described in detail below:

[0062] S100: Acquire an input image, wherein the input image is in png format.

[0063] S200: Editing a classification rule, and clustering the input image into three categories, namely, category A, category B, and category C, based on the classification rule;

[0064] The division rules are:

[0065] The average grayscale of the three channels in the input image is calculated by using the average grayscale calculation formula, and a grayscale threshold is configured. If the average grayscale of the input image is less than the grayscale threshold, the input image is classified into category A;

[0066] With the grayscale value corresponding to the 8-bit digital image as the horizontal coordinate and the number of pixels corresponding to each grayscale value as the vertical coordinate, a three-channel histogram is drawn to define the high-gray stage, traverse the maximum proportion of the high-gray stage in the three-channel histogram, and calculate the overlapping area of ​​the high-gray stage in the three-channel histogram, and define the input image with the maximum proportion of the high-gray stage greater than 0.025 or the overlapping area less than 0.015 as Class B;

[0067] Input images other than class A and class B are defined as class C.

[0068] S300: receiving an image to be processed input by a user, and determining whether the image to be processed belongs to category A.

[0069] If yes, directly output the image to be processed; if no, continue to determine whether the image to be processed belongs to category B;

[0070] If yes, calculate the pixel grayscale of each pixel, establish the corresponding relationship between the pixel grayscale and the reduction coefficient, reduce the pixel grayscale based on the corresponding relationship, process the reduced image to be processed using a preset low-power scheme, and output it; if no, continue to determine whether the image to be processed belongs to category C;

[0071] If so, a correction factor is selected, the correction factor and the histogram correction formula are integrated, the three-channel histogram is corrected to obtain an intermediate image, the image to be processed is processed using a histogram matching algorithm, and the intermediate image is used for deviation to obtain a result image and output it.

[0072] According to the average grayscale of the input image, the category to which the input image belongs is determined; if the average grayscale GLav of the input image is less than the threshold GLth, it is an image with lower brightness (category A). The power consumption of this image is reduced to a small extent, and in order to maintain the visual effect, low-power processing is not necessary.

[0073] If the maximum proportion kh in the high-gray stage is greater than 0.025 or the histogram overlapping area OAh in the high-gray stage is less than 0.015, it means that the pixel distribution of the three channels in the high-gray stage is seriously unbalanced (Class B). If the histogram correction method is used for such images, although it can reduce power consumption, color distortion will occur. Therefore, it is necessary to select a low-power solution in the prior art for processing; the specific processing includes: if only power consumption needs to be reduced, the proportional algorithm NPC should be selected, and the grayscale of all pixels should be multiplied by a coefficient between 0 and 1. If the visual effect of the image after low-power processing needs to be improved, the ACSC low-power solution should be adopted.

[0074] If there is no serious imbalance in the pixel distribution of the three channels in the high gray stage of the input image (Class C), the histogram correction and matching method will be used to reduce power consumption; that is, firstly, the histogram correction is performed to reduce the number of pixels in the high grayscale part, and then the histogram matching is performed to make the histogram of the input image similar to the corrected histogram, and the result image is obtained and output.

[0075] In Example 2, S200 is described in detail as follows:

[0076] The average grayscale calculation formula is:

[0077]

[0078] Where GLav is the average grayscale, W and H are the horizontal and vertical resolutions of the input image, respectively, and R(i,j), G(i,j) and B(i,j) are the grayscales of the pixels in the i-th row and j-th column in the red, green and blue channels, respectively.

[0079] By calculation, the average grayscale of the input image, that is, the average brightness value, can be obtained, which represents the overall brightness characteristics of the image.

[0080] In Example 3, the step of "configuring a grayscale threshold, and if the average grayscale of the input image is less than the grayscale threshold, classifying the input image into category A" is described in detail as follows:

[0081] S201: Mark the grayscale threshold as 80, recorded as GLth=80.

[0082] The grayscale threshold is a value used to distinguish different gray levels in the input image. Each pixel of the input image has a grayscale value, usually in the range of 0 to 255, and 80 is set as the grayscale threshold.

[0083] S202: If GLav<GLth=80 of the input image, the input image is classified into category A.

[0084] The input image is clustered using the grayscale threshold. If the average grayscale of the input image is less than the grayscale threshold, the input image is classified into category A.

[0085] In Example 4, the step of "defining the high gray stage" is described in detail as follows:

[0086] S203: traverse to find the maximum grayscale value in the three-channel histogram and record it as GLmax.

[0087] Construct a histogram for each channel, that is, construct histograms for the three channels of R, G and B respectively, where the histogram of each channel represents the distribution of the grayscale values ​​of each pixel in the channel, the abscissa of the histogram represents the grayscale value (usually from 0 to 255), and the ordinate represents the number of times the grayscale value appears (that is, the number of pixels), find the maximum grayscale value in the three histograms, and the maximum grayscale value represents the strongest brightness in the input image.

[0088] S204: defining the portion of the three-channel histogram whose grayscale value is within the range of 0.8×GLmax to GLmax as a high gray stage.

[0089] The maximum grayscale value is used to divide the high-gray stage, where the high-gray stage is a set of pixels with relatively high grayscale values ​​in the input image. During the display process, such a set of pixels will generate greater power consumption, resulting in a shortened battery life of the device. Therefore, adjustments to such a set of pixels can reduce power consumption while ensuring display effects.

[0090] In Example 5, the step of "traversing to find the maximum proportion of the high gray stage in the three-channel histogram" is described in detail as follows:

[0091] The maximum proportion formula is used to calculate the maximum proportion of the high gray stage in the three-channel histogram, which is recorded as kh;

[0092] The maximum proportion formula is:

[0093]

[0094] If kh>0.025, it means that the three-channel histogram in the high-gray stage is seriously unbalanced, and it needs to be classified into category B for processing.

[0095] In Example 6, the step of "calculating the overlapping area of ​​the high gray stage in the three-channel histogram" is described in detail as follows:

[0096] If the input image does not belong to category A, the overlapping area calculation formula is used to calculate the overlapping area of ​​the high gray stage in the three-channel histogram, which is recorded as OAh;

[0097] The overlapping area calculation formula is:

[0098]

[0099] In addition to classifying input images with kh>0.025 into category B, input images with OAh<0.015 also need to be classified into category B. The overlapping area of ​​the three-channel histogram is an important parameter for analyzing color distortion. For images with color distortion in the high-gray stage, the OAh value will become smaller. Therefore, by calculating OAh, the degree of color distortion in the high-gray stage can be determined.

[0100] In Example 7, the step of "drawing a three-channel histogram with the grayscale value corresponding to the 8-bit digital image as the horizontal coordinate and the number of pixels corresponding to each grayscale value as the vertical coordinate" is described in detail as follows:

[0101] In each channel, the histogram statistical formula is used to calculate the number of pixels corresponding to each gray value in turn, and then integrated to generate a three-channel histogram;

[0102] The histogram statistical formula is:

[0103] HkD i =card{p|gl r =i},i=0,1,...,255

[0104] HkD i =card{p|gl g =i},i=0,1,...,255

[0105] HkDJ i =card{p|gl b =i},i=0,1,...,255

[0106] Among them hR i Indicates the number of pixels with grayscale value i in the red channel of the input image, where i is an integer in the range of 0 to 255, p is a pixel of the input image, and gl r is the grayscale value of the red channel of pixel p.

[0107] A histogram is a statistical chart showing the distribution of each grayscale level. The horizontal axis represents the grayscale value, which is composed of integers in the range of [0,255] of an 8-bit digital image. The vertical axis represents the number of pixels of the corresponding grayscale level in the input image. By using the histogram statistical formula, the number of pixels of each grayscale value in each color channel of the input image can be calculated, thereby drawing a histogram for each channel.

[0108] In Example 8, the step of “limiting the pixel grayscale” is described in detail as follows:

[0109] The pixel grayscale is narrowed by using the narrowing coefficient, wherein the range of the narrowing coefficient is 0-1.

[0110] In Example 9, the step of "selecting a correction factor, integrating the correction factor and the histogram correction formula, correcting the three-channel histogram to obtain an intermediate image, applying a histogram matching algorithm to process the image to be processed, and deviating the intermediate image to obtain a result image, and outputting it" is described in detail as follows:

[0111] Based on the average grayscale, a correction factor is selected and recorded as k;

[0112] The selection process is:

[0113]

[0114] Substituting k into the adaptive histogram correction formula, and correcting the three-channel histogram;

[0115] The adaptive histogram correction formula is:

[0116]

[0117]

[0118] Wherein C represents the color channel of the image to be processed, and C is R, G or B; histC i is the histogram of channel C, histC i ' is the histogram of channel C after correction, is the histogram of channel C after offset, i is the grayscale value, 0≤i≤255, Cmax is the maximum grayscale value of channel C, DC 1 is the first correction distance, DC 2 is the second corrected distance, C start is the minimum grayscale value after correction.

[0119] In this embodiment, histogram correction is performed to reduce the number of pixels in the high-gray stage, thereby reducing the power consumption of the input image. The correction factor k of the histogram is mainly aimed at the high-gray stage. The smaller k is, the more the number of pixels in the high-gray stage is reduced, and the greater the reduction in power consumption. The size of the correction factor k is determined by the size of the average grayscale GLav; in other words, the greater the average brightness of the input image, the greater the degree of correction.

[0120] While correcting the high gray stage, it is also necessary to maintain the visual effect of the low gray level part. Based on this, the correction distance DC is constructed. 1 and Corrected Distance DC 2 ; C startThe starting grayscale of the histogram correction, which is the minimum grayscale plus the correction distance DC 1 , less than C start The histogram of the value is not corrected to maintain the visual effect of the low grayscale part; for the histogram greater than this value, DC 2 To correct the distance, the DC close to the maximum grayscale value Cmax of the C channel 2 The segment histogram is multiplied by a correction factor k, where k is determined according to the selection process.

[0121] Set the grayscale C close to the starting point of the C channel start DC 2 The segment histogram is superimposed with the reduced histogram of the high gray stage to ensure that the overall number of pixels in the image does not change.

[0122] In this embodiment, Figure 2 For example, the correction factors k = 0.625 and k = 0.4375, Figure 2 (a1) and Figure 2 (a2) is modified, where Figure 2 (a1) is the original image, Figure 2 (a2) is the histogram of the original image. Figure 2 (b1) and Figure 2 (b2) is the histogram generated by the low power consumption graph and the corresponding correction factor k = 0.625. Figure 2 (c1) and Figure 2 (c2) is the low power consumption graph and the corresponding histogram generated by the correction factor k = 0.4375. It can be seen that the smaller the correction factor, the smaller the number of pixels retained in the high gray stage, and the greater the power reduction.

[0123] In Example 10, the step of "applying a histogram matching algorithm to process the image to be processed, and using the intermediate image to deviate, obtaining a result image, and outputting it" is described in detail as follows:

[0124] The image to be processed is shifted toward the intermediate image by using a histogram matching algorithm in the prior art, and a result image is generated.

[0125] In actual use, 300 images of the BSD300 image dataset are taken as an example for low-power processing, where GLth = 80;

[0126] Read each image in the folder BSD300_number respectively, and calculate the average grayscale GLav of the three channels of the input image. If GLav﹤GLth=80, classify it as a category that does not need to be processed, that is, category A.

[0127] Otherwise, the values ​​of kh and OAh are calculated respectively. If kh>0.025 or OAh<0.015, the histogram of the three channels in the high grayscale part is seriously unbalanced and it is classified as class B.

[0128] Otherwise, the three channels of the high grayscale part are not seriously unbalanced, and it is classified as a C-type image.

[0129] For category A images, output directly.

[0130] For the Class B images, the equal-proportional power consumption solution or the color space conversion low-power consumption solution in the prior art is used for processing (in this embodiment, ACSC is selected as the low-power consumption solution).

[0131] For class C images, a low-power solution based on histogram correction and histogram matching is adopted, which includes the following steps.

[0132] 1. Calculate the correction factor k;

[0133] 2. Correct the histogram according to the k value;

[0134] 3. Generate the result image through histogram matching.

[0135] The input image is processed by applying a histogram matching algorithm, that is, the histogram of the input image is matched to a degree similar to the corrected histogram to generate a result image.

[0136] After processing, different algorithms are used to limit the power consumption of the BSD300 image dataset and calculate performance indicators, among which PD 1 is the power consumption value of the input image calculated according to the power consumption model, PD 2 is the power consumption value of the output image, MSE represents the information loss of the output image relative to the input image, and RT represents the retention rate of the highlighted pixels;

[0137]

[0138] in, The reduction in power consumption, that is, the reduction in the display power consumption of the processed image compared to that before processing. The larger the index, the better.

[0139]

[0140] MSE is used to evaluate the information loss of the image before and after power reduction. The smaller the MSE, the weaker the information loss and the better the performance. Since the reduction of power consumption is achieved by adjusting the grayscale of each pixel unit, it will inevitably lead to information loss. Reducing power consumption while reducing information loss is the key to power reduction technology.

[0141] C start =Cmin +0.8×(C max -C min );

[0142] NC 1 =card{p|glC 1 ∈[C start ,C max ]};

[0143] NC 2 =card{p|glC 2 ∈[C start ,C max ]};

[0144]

[0145] Since the color and brightness information of the high gray stage will suppress the low gray part, the number of pixels in the high gray stage has a decisive influence on the visual effect of the image; therefore, RTH (retention rate of high gray pixels) is used to evaluate the visual effect of the image after consumption reduction processing; where C represents the three primary color channels, which can be R, G or B. The high gray stage refers to [C start ,Cmax];NC 1 and NC 2 They are the number of pixels in the high gray stage before and after processing respectively; RTH is the ratio of the sum of the number of pixels in the high gray stage in the three channels before and after processing. The larger the value is, the higher the retention rate of the high gray scale is, and the better the visual effect is.

[0146] Finally, the comprehensive performance of image power reduction processing is evaluated using the comprehensive power reduction index CPC.

[0147]

[0148] This indicator consists of three parts: the reduction in power consumption Pixel retention rate RTH and information loss MSE in high gray stage. When is larger, RTH is larger and MSE is smaller, CPC is larger. The larger the CPC is, the better the energy saving performance is.

[0149]

[0150] Different energy-saving methods are used to process 300 images of the BSD300 dataset, and the average values ​​of the above performance indicators are calculated. It can be seen that the CPC value of the proposed selective adaptive low-power method is the largest, indicating that the method has the best comprehensive energy-saving performance.

[0151] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0152] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0153] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A selective adaptive low-power image display method, characterized in that: The method comprises: S100: Acquire an input image, wherein the input image is in png format; S200: Editing a classification rule, and clustering the input image into three categories, namely, category A, category B, and category C, based on the classification rule; The division rules are: The average grayscale of the three channels in the input image is calculated by using the average grayscale calculation formula, and a grayscale threshold is configured. If the average grayscale of the input image is less than the grayscale threshold, the input image is classified into category A; With the grayscale value corresponding to the 8-bit digital image as the horizontal coordinate and the number of pixels corresponding to each grayscale value as the vertical coordinate, a three-channel histogram is drawn to define the high-gray stage, traverse the maximum proportion of the high-gray stage in the three-channel histogram, and calculate the overlapping area of ​​the high-gray stage in the three-channel histogram, and define the input image with the maximum proportion of the high-gray stage greater than 0.025 or the overlapping area less than 0.015 as Class B; The input images other than those in categories A and B are defined as category C; S300: receiving an image to be processed input by a user, and determining whether the image to be processed belongs to category A. If yes, directly output the image to be processed; if no, continue to determine whether the image to be processed belongs to category B; If yes, calculate the pixel grayscale of each pixel, establish the corresponding relationship between the pixel grayscale and the reduction coefficient, reduce the pixel grayscale based on the corresponding relationship, process the reduced image to be processed using a preset low-power scheme, and output it; if no, continue to determine whether the image to be processed belongs to category C; If so, a correction factor is selected, the correction factor and the histogram correction formula are integrated, the three-channel histogram is corrected to obtain an intermediate image, the image to be processed is processed using a histogram matching algorithm, and the intermediate image is used for deviation to obtain a result image and output it.

2. The selective adaptive low power consumption image display method according to claim 1, characterized in that: The average grayscale calculation formula in S200 is: Where GLav is the average grayscale, W and H are the horizontal and vertical resolutions of the input image, respectively, and R(i,j), G(i,j) and B(i,j) are the grayscale values ​​of the pixels in the i-th row and j-th column in the red, green and blue channels, respectively.

3. The selective adaptive low power consumption image display method according to claim 1, characterized in that: The step of "configuring a grayscale threshold, and if the average grayscale of the input image is less than the grayscale threshold, classifying the input image into category A" includes: The grayscale threshold is marked as 80, denoted as GLth=80; If GLav﹤GLth=80 of the input image, the input image is classified into class A.

4. The selective adaptive low power consumption image display method according to claim 1, characterized in that: The step of "defining a high gray stage" includes: Traverse the maximum grayscale value in the three-channel histogram and record it as GLmax; In the three-channel histogram, a portion whose grayscale value is within the range of 0.8×GLmax to GLmax is defined as a high gray stage.

5. The selective adaptive low power consumption image display method according to claim 4, characterized in that: The step of "traversing to find the maximum proportion of the high gray stage in the three-channel histogram" includes: The maximum proportion formula is used to calculate the maximum proportion of the high gray stage in the three-channel histogram, which is recorded as kh; The maximum proportion formula is:

6. The selective adaptive low power consumption image display method according to claim 1, characterized in that: The step of "calculating the overlapping area of ​​the high gray stage in the three-channel histogram" includes: If the input image does not belong to category A, the overlapping area calculation formula is used to calculate the overlapping area of ​​the high gray stage in the three-channel histogram, which is recorded as OAh; The overlapping area calculation formula is:

7. The selective adaptive low power consumption image display method according to claim 1, characterized in that: The step of "drawing a three-channel histogram with the grayscale value corresponding to the 8-bit digital image as the horizontal coordinate and the number of pixels corresponding to each grayscale value as the vertical coordinate" includes: In each channel, the histogram statistical formula is used to calculate the number of pixels corresponding to each gray value in turn, and then integrated to generate a three-channel histogram; The histogram statistical formula is: hR i =card{p|gl r =i},i=0,1,...,255 hG i =card{p|gl g =i},i=0,1,...,255 hB i =card{p|gl b =i},i=0,1,...,255 Among them hR i Indicates the number of pixels with grayscale value i in the red channel of the input image, where i is an integer in the range of 0 to 255, p is a pixel of the input image, and gl r is the grayscale value of the red channel of pixel p.

8. The selective adaptive low power consumption image display method according to claim 1, characterized in that: The step of "limiting the pixel grayscale" comprises: The pixel grayscale is narrowed by using the narrowing coefficient, wherein the range of the narrowing coefficient is 0-1.

9. The selective adaptive low power consumption image display method according to claim 3, characterized in that: The step of "selecting a correction factor, integrating the correction factor and the histogram correction formula, correcting the three-channel histogram to obtain an intermediate image, applying a histogram matching algorithm to process the image to be processed, deviating the intermediate image, obtaining a result image, and outputting it" includes: Based on the average grayscale, a correction factor is selected and recorded as k; The selection process is: Substituting k into the adaptive histogram correction formula, and correcting the three-channel histogram; The adaptive histogram correction formula is: DC1=(C max -C min )×0.1875; DC2=(C max -C min )×0.40625; C start =C min +DC1; Wherein C represents the color channel of the image to be processed, and C is R, G or B; histC i is the histogram of channel C, histC i ' is the histogram of channel C after correction, is the histogram of channel C after offset, i is the grayscale value, 0≤i≤255, Cmax is the maximum grayscale value of channel C, DC1 is the first correction distance, DC2 is the second correction distance, C start is the minimum grayscale value after correction.

10. The selective adaptive low power consumption image display method according to claim 1, characterized in that: The step of "applying a histogram matching algorithm to process the image to be processed, and using the intermediate image to deviate, obtaining a result image, and outputting it" includes: The image to be processed is shifted toward the intermediate image by using a histogram matching algorithm in the prior art, and a result image is generated.

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