Image processing method, device, electronic device, and computer-readable storage medium

Abstract

The present application relates to an image processing method, device, electronic device, and computer-readable storage medium. The image processing method includes: obtaining a user's enhancement preference information; obtaining an image to be processed, and dividing the image to be processed into multiple areas to be enhanced according to the hue distribution of the image to be processed; and performing corresponding color enhancement processing on each of the areas to be enhanced according to the enhancement preference information to adjust the color attributes of the image to be processed. By dividing the image to be processed into multiple areas to be enhanced based on the hue distribution, corresponding color enhancement processing can be performed for different hues, thereby effectively avoiding the problem of color distortion in the process of color enhancement. Moreover, combined with the obtained user enhancement preference information, a personalized and accurate color enhancement effect can be provided based on the user's preferences, thereby realizing an image processing method with a high degree of personalization and high color reproduction.

Description

Technical Field

[0001] The present application relates to the field of display technology, and in particular to an image processing method, device, electronic device, and computer-readable storage medium. Background Art

[0002] With the advancement of display technology, display devices are capable of displaying increasingly rich colors. Users have different color preferences, such as some prefer bright colors, while others prefer more natural colors. However, the image processing methods currently used in display devices are limited and cannot flexibly adapt to different user preferences, resulting in a poor viewing experience. Summary of the Invention

[0003] Based on this, it is necessary to provide an image processing method, device, electronic device, computer-readable storage medium and computer program product that can flexibly and accurately adjust the color enhancement effect in response to the above technical problems.

[0004] An image processing method, comprising:

[0005] Obtaining enhanced preference information of users;

[0006] Acquire an image to be processed, and divide the image to be processed into a plurality of areas to be enhanced according to the tone distribution of the image to be processed;

[0007] According to the enhancement preference information, corresponding color enhancement processing is performed on each of the to-be-enhanced areas to adjust the color attribute of the to-be-processed image.

[0008] An image processing device, comprising:

[0009] A preference acquisition module is used to obtain the user's enhanced preference information;

[0010] A region division module is used to obtain an image to be processed and divide the image to be processed into a plurality of regions to be enhanced according to the tone distribution of the image to be processed;

[0011] The enhancement processing module is used to perform corresponding color enhancement processing on each of the to-be-enhanced areas according to the enhancement preference information, so as to adjust the color attributes of the to-be-processed image.

[0012] A display device, comprising:

[0013] Display screen;

[0014] A processor is connected to the display screen and is used to obtain the user's enhancement preference information; obtain an image to be processed, and divide the image to be processed into multiple areas to be enhanced according to the hue distribution of the image to be processed; perform corresponding color enhancement processing on each of the areas to be enhanced according to the enhancement preference information to adjust the color attributes of the image to be processed; and send the image after the color attributes are adjusted to the display screen for display.

[0015] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0016] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0017] The above-mentioned image processing method, device, electronic device, and computer-readable storage medium can divide the image to be processed into multiple areas to be enhanced based on the hue distribution of the image to be processed, and can perform corresponding color enhancement processing for different hues, thereby effectively avoiding the problem of color distortion in the process of color enhancement. Moreover, combined with the acquired user enhancement preference information, it is possible to provide personalized and precise color enhancement effects based on the user's preferences. Therefore, in the embodiment of the present application, accurate color enhancement processing can be performed based on the hue distribution in the image to be processed and the user's enhancement preference information, thereby realizing an image processing method with a high degree of personalization and high color reproduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flowchart of an image processing method according to an embodiment;

[0019] Figure 2 is a sub-flowchart of step 102 of one embodiment;

[0020] Figure 3 This is a second flowchart of an image processing method according to an embodiment;

[0021] Figure 4 This is a third flowchart of an image processing method according to an embodiment;

[0022] Figure 5 This is a fourth flowchart of an image processing method according to an embodiment;

[0023] Figure 6 A diagram showing the internal structure of a display device according to an embodiment;

[0024] Figure 7 FIG. 1 is a diagram showing the internal structure of an electronic device according to an embodiment. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0026] The image processing method provided in the embodiment of the present application can be applied to a display device. Among them, the display device can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The display device includes at least a display screen for displaying images and a processor for processing image data. Specifically, the processor may include a GPU (Graphics Processing Unit), which is used to perform mathematical and geometric calculations and graphics rendering to achieve the required image processing functions.

[0027] Figure 1 This is a flowchart of an image processing method according to an embodiment. Figure 1 In this embodiment, the image processing method includes steps 102 to 106.

[0028] Step 102: Obtain the user's enhanced preference information.

[0029] Among them, the user's enhancement preference information refers to the user's preference information for color enhancement. Specifically, the user's physiological phenomena and / or psychological feelings may lead to different preferences for different color enhancement methods. Physiological phenomena refer to the phenomenon that for the same objective quantity of color, under the same observation environment, the colors observed by the human eye are not exactly the same. Exemplarily, physiological phenomena that can lead to differences in observed colors include, for example, race, age, color vision disorder, etc. Psychological feelings refer to the user's subjective feelings about different colors. For example, some people like bright colors, and some people like natural colors. Moreover, people who like bright colors may not like all colors, but may like natural skin colors and bright blue sky and trees.

[0030] Exemplarily, the user's enhancement preference information can be obtained based on historical data. For example, the user's historical image editing operations in the system image processing software or third-party image processing software can be obtained, and the user's enhancement preference information can be obtained based on the user's adjustment of the color during the editing operation. The user's adjustment of the color can specifically be the adjustment of each color attribute. If the user chooses to increase the brightness of the red channel at a higher frequency when making adjustments, it means that the user prefers the red channel to use a brighter color. In another exemplary embodiment, some questions or pictures related to color enhancement preferences can also be provided to the user, and the enhancement preference information can be obtained based on the selection results provided by the user. It can be understood that the above-mentioned method of obtaining the user's enhancement preference information is only for exemplary description, and is not used to limit the protection scope of this embodiment. Other methods that can obtain the user's enhancement preference information can also be applied to this embodiment. Therefore, by determining the user's enhancement preference information in a certain way, it is possible to provide a personalized and accurate color enhancement effect based on the user's preferences.

[0031] Step 104 : Acquire the image to be processed, and divide the image to be processed into a plurality of areas to be enhanced according to the tone distribution of the image to be processed.

[0032] Specifically, as described above, the brightness of different colors varies from user to user. Therefore, using the same color enhancement method for different colors will not produce optimal color enhancement results. For example, consider a user's preference for natural skin tones and vibrant scenery. Applying the same color enhancement parameters for skin tones to the entire image will result in insufficient brightness in the scenery area, meaning the scenery area will lack vibrant color. Applying the same color enhancement parameters for scenery to the entire image will result in excessive brightness in the skin tones area, meaning the skin tones will be distorted. Hue refers to a color attribute that describes the simple appearance of a color or can be understood as its position on the color spectrum. Therefore, different colors can be understood as having different hues. By dividing the image to be processed into multiple enhancement areas based on hue, we can effectively avoid the problem of achieving a single enhancement effect for different colors when the same processing parameters are used for different hues, thereby further improving the color enhancement effect.

[0033] Step 106 : performing corresponding color enhancement processing on each to-be-enhanced region according to the enhancement preference information, so as to adjust the color attributes of the to-be-processed image.

[0034] Specifically, the processor can be pre-configured with a mapping relationship between different tones and color enhancement processing parameters so that during the image processing process, corresponding color enhancement processing can be performed on different areas to be enhanced. That is, for an area to be enhanced with a first hue, the corresponding first parameter is selected for color enhancement processing; for an area to be enhanced with a second hue, the corresponding second parameter is selected for color enhancement processing, and so on. Moreover, by combining enhancement preference information, more accurate color enhancement processing can be achieved. For example, the first hue can be configured with multiple different color enhancement processing parameters, and different parameters correspond one-to-one to different enhancement preference information.

[0035] In this embodiment, by dividing the image to be processed into multiple areas to be enhanced based on its hue distribution, corresponding color enhancement processing can be performed for different hues, thereby effectively avoiding color distortion during the color enhancement process. Moreover, combined with the acquired user enhancement preference information, a personalized and precise color enhancement effect can be provided based on the user's preferences. Therefore, in this embodiment of the application, accurate color enhancement processing can be performed based on the hue distribution in the image to be processed and the user's enhancement preference information, thereby realizing an image processing method with a high degree of personalization and high color reproduction.

[0036] Figure 2 This is a sub-flowchart of step 102 of an embodiment, refer to Figure 2 In one embodiment, the step of obtaining the user's enhanced preference information includes steps to steps.

[0037] Step 202: Generate a candidate image set.

[0038] The alternative image set includes multiple images generated by applying multiple different color enhancement processes to the same image. The alternative image set is used to be displayed on a user interface (UI), and the user can interact by clicking on a trigger area on the interface. Optionally, the alternative image set can be generated in response to a preset operation acting on the user interface. The preset operation can be, for example, a user clicking a color personalization button or a user performing a gesture corresponding to color personalization, etc., which is not limited in this embodiment.

[0039] Each candidate image set can include two, three, or four images, depending on the accuracy and speed of analyzing the enhanced preference information. Multiple images in the same candidate image set can be used to test a user's specific preferences for certain color attributes. For example, the saturation of the blue channel of the same image can be adjusted to generate three images in the same candidate image set. For example, the saturation of the blue channel can be set to 100%, 95%, and 90%, respectively, while the parameters of the other color channels remain unchanged. The initial image used to generate the candidate image set can be a color patch image, that is, the initial image includes a color patch of the color associated with the color attribute to be tested. The initial image can also be a photograph that meets preset conditions. For example, if you need to test color attributes related to blue tones, you can use an image of the sky or ocean as the initial image; if you need to test color attributes related to green tones, you can use an image of plants as the initial image. Optionally, multiple images in the same candidate image set can be displayed simultaneously or sequentially on the user interface, which is not limited in this embodiment.

[0040] Furthermore, when displaying a selection set of candidate images, the user can be prompted to select based on their personal preferences, thereby obtaining the user's preferred selection results in a timely manner. For example, when displaying multiple photos in a selection set of candidate images, the user can be informed of the image selection rules and prompted to make a selection through text prompts. For example, the text prompts could include "Please select your most preferred image," "Please select your least preferred image," and "Please sort the images according to your preference," thereby achieving a user-friendly user interface.

[0041] Optionally, when multiple candidate image sets need to be generated, they can be generated simultaneously to simplify the generation rules for the candidate image sets; alternatively, multiple candidate image sets can be generated sequentially. If multiple candidate image sets are generated sequentially, multiple images in subsequent candidate image sets can be adaptively generated based on the preference selection results of previous candidate image sets, thereby improving the accuracy of the analysis of enhanced preference information.

[0042] Step 204 : obtaining the user's preference selection results for the multiple images in each candidate image set, and obtaining enhanced preference information based on the preference selection results.

[0043] Specifically, when obtaining enhanced preference information based on the user's preference selection results for the candidate image set, the information can be matched to the image selection rules previously communicated to the user. For example, if the selection rule is to select preferred images, the color attributes corresponding to the selected images can be enhanced; if the selection rule is to select disliked images, the color attributes corresponding to the selected images can be weakened.

[0044] Understandably, ordinary users don't have a detailed understanding of color attributes. Without the visual presentation of this embodiment, and simply providing controls such as sliders for adjusting attribute parameters, users wouldn't understand the effects of these adjustments, nor would they know if any side effects would occur. Consequently, users wouldn't be able to accurately adjust the color enhancement effect. Therefore, in this embodiment, by providing a set of alternative images, users can gain a more intuitive understanding of different color enhancement processes, enabling more accurate selection and obtaining more accurate enhancement preference information. This effectively improves user convenience.

[0045] In one embodiment, there are multiple alternative image sets, wherein the color enhancement processing parameters corresponding to the multiple images in the n+1th alternative image set are determined by the preference selection results of the nth alternative image set. Specifically, this embodiment may include the following steps when generating the alternative image set. First, a first alternative image set is generated, and then, based on the user's preference selection results for the multiple images in the nth alternative image set, multiple color enhancement processing parameters for the n+1th alternative image set are determined and the corresponding multiple images are generated, where n is a positive integer. The multiple alternative image sets are used to be displayed sequentially on the user interface, that is, the nth alternative image set is first displayed on the user interface, and after the user provides the preference selection results for the multiple images in the nth alternative image set, the multiple images in the n+1th alternative image set are determined, and the n+1th alternative image set is displayed on the user interface, until all the alternative image sets are displayed. For example, if the user's preference for multiple images in the first candidate image set is that they prefer images with higher blue saturation, then when generating the second candidate image set, the saturation of the blue channel of each image can be adjusted to the user's preference, and the red channel of each image can be adjusted to have a different saturation, thereby more quickly and accurately obtaining the user's enhanced preference information. It is understood that in some embodiments, multiple candidate image sets can simply be displayed sequentially, and the multiple candidate image sets can be generated independently of each other.

[0046] In one embodiment, the number of candidate image sets is two. Specifically, Figure 3 This is a second flow chart of an image processing method according to an embodiment, referring to Figure 3 In this embodiment, the image processing method includes steps 302 to 310. The specific implementation of steps 306 to 310 can refer to the previous embodiment and will not be repeated here. Step 202 in the previous embodiment can include steps 302 to 304 of this embodiment.

[0047] Step 302: Generate a first candidate image set based on the first initial image with a blue-green tone.

[0048] The appropriate first initial image can be determined based on the ratio of the number of pixels displaying blue and green tones to the total number of pixels on the display screen. Specifically, this ratio can be 90%, meaning that 90% of the pixels on the display screen are used to display blue-green tones. For example, the first initial image can be an image of trees against a blue sky. By performing various color enhancement processes on this image of trees against a blue sky, the desired first candidate image set can be generated, thereby capturing the user's preference for blue-green tones.

[0049] Step 304 : Generate a second candidate image set according to the second initial image with red, green and blue tones and the second parameter.

[0050] Among them, the appropriate second initial image can be determined based on the proportion of the number of pixels displayed in the red, green, and blue color hues to the total number of pixels in the display screen. Specifically, if the number of pixels displayed in red tones in the image accounts for 25% to 35% of the total number of pixels in the display screen, the number of pixels displayed in green tones also accounts for 25% to 35% of the total number of pixels in the display screen, and the number of pixels displayed in blue tones accounts for 25% to 35% of the total number of pixels in the display screen, then it can be considered that the image can be used as the second initial image. Exemplarily, the second initial image can be, for example, an image containing fruits in colors such as red, green, and blue. By performing a variety of different color enhancement processes on the image containing fruits in colors such as red, green, and blue, a second set of alternative images can be generated. The second set of alternative images is used to obtain the user's preference for red, green, and blue tones.

[0051] Step 306 : Obtain the user's preference selection results for the multiple images in each candidate image set, and obtain enhanced preference information based on the preference selection results.

[0052] Step 308: Acquire the image to be processed, and divide the image to be processed into a plurality of areas to be enhanced according to the tone distribution of the image to be processed.

[0053] Step 310 : performing corresponding color enhancement processing on each area to be enhanced according to the enhancement preference information to adjust the color attributes of the image to be processed.

[0054] Furthermore, in one of the embodiments, second parameters for color enhancement processing can be determined based on the user's preference selection results for multiple images in the first alternative image set, and a second alternative image set can be generated based on the determined second parameters and the second initial image with red, green and blue tones, thereby improving the pertinence of the second alternative image set and the accuracy of the selection results.

[0055] In one embodiment, multiple images in the candidate image set are each configured with a corresponding plurality of image enhancement coefficients. The image enhancement coefficients correspond to color enhancement processing parameters. That is, if a user selects an image generated based on a certain color enhancement processing parameter, it can be understood that the user has selected the image enhancement coefficient corresponding to that parameter as a preferred selection result. Accordingly, if a target enhancement coefficient ratio is determined based on the user's preferred selection result, the color enhancement processing parameter corresponding to the target enhancement coefficient ratio can also be determined, and the image to be processed can be processed accordingly. Figure 4 This is a flowchart of the image processing method according to an embodiment. Figure 4 In one embodiment, the image processing method includes steps 402 to 412. The specific implementations of steps 402 to 404 and steps 410 to 412 can be referred to in the previous embodiment and will not be repeated here. That is, the steps of obtaining enhanced preference information based on the preference selection result in the previous embodiment specifically include steps 406 to 408 of this embodiment.

[0056] Step 402: Generate a candidate image set.

[0057] Step 404: Obtain the user's preference selection results for multiple images in each candidate image set.

[0058] Step 406: Obtain candidate enhancement coefficients corresponding to each candidate image set.

[0059] The alternative enhancement coefficient is the image enhancement coefficient corresponding to an image selected by the user from the alternative image set. For example, take the case where there are two alternative image sets, each of which includes three images. The image enhancement coefficients of the three images in the first alternative image set are 0.2, 0.5, and 0.8, respectively, and the image enhancement coefficients of the three images in the second alternative image set are 0.2, 0.7, and 0.9, respectively. If the user selects an image with an image enhancement coefficient of 0.5 in the first alternative image set, the first alternative enhancement coefficient is 0.5. If the user selects an image with an image enhancement coefficient of 0.7 in the second alternative image set, the second alternative enhancement coefficient is 0.7.

[0060] In step 408, a target enhancement coefficient ratio is obtained as enhancement preference information based on the multiple alternative enhancement coefficients. For example, based on the two alternative enhancement coefficients obtained in the previous step, the average value of the two, 0.6, can be used as the target enhancement coefficient ratio for subsequent color enhancement processing. It is understandable that the average method for obtaining the target enhancement coefficient ratio can be, but is not limited to, arithmetic average, weighted average, etc. Among them, by avoiding the value of the image enhancement coefficient, it is possible to avoid the situation where the same target enhancement coefficient ratio is obtained after selecting different images, so as to improve the reliability of the obtained target enhancement coefficient ratio, thereby improving the reliability of the image processing method.

[0061] Step 410: Acquire an image to be processed, and divide the image to be processed into a plurality of areas to be enhanced according to the tone distribution of the image to be processed.

[0062] Step 412 : performing corresponding color enhancement processing on each to-be-enhanced region according to the enhancement preference information, so as to adjust the color attributes of the to-be-processed image.

[0063] In this embodiment, by setting the enhancement coefficient, a connection can be established between the user's personalized selection and the color enhancement processing parameters. Thus, by performing a relatively simple calculation on the preferred selection results of multiple candidate image sets, the required target enhancement coefficient can be obtained, and accurate color enhancement processing can be achieved using the obtained target enhancement coefficient.

[0064] In one embodiment, the step of obtaining the user's enhancement preference information may further include the following steps. If a coefficient adjustment signal acting on a designated trigger area of ​​the interface is received, the target enhancement coefficient ratio is updated according to the coefficient adjustment signal. Exemplarily, the designated trigger area may be, for example, a slider area on the user interface. The slider area is provided with a plurality of scales, and the user can select any scale on the slider by dragging. Among them, each scale on the slider corresponds to each target enhancement coefficient ratio. For example, the slider may be provided with 256 scales, and the 256 scales correspond one-to-one to 256 target enhancement coefficient ratios, and the 256 target enhancement coefficient ratios correspond one-to-one to 256 color enhancement processing parameters. Therefore, by selecting a scale, a corresponding color enhancement processing parameter can be selected, thereby realizing the selection of the color enhancement effect.

[0065] Furthermore, a preliminary target enhancement coefficient ratio can be determined based on the alternative image sets. For example, if there are two alternative image sets, and each alternative image set includes three images, the preliminary target enhancement coefficient ratio can be determined to be one of nine values. Moreover, the target enhancement coefficient ratio preliminarily determined through the above steps is nine of the 256 target enhancement coefficient ratios corresponding to the 256 scales, thereby preliminarily determining the value range of the target enhancement coefficient ratio. Based on the preliminarily determined value range, the user can further refine the adjustment through the slider, thereby achieving fine-tuning of the color enhancement effect. In this embodiment, by combining the above two methods for determining the target enhancement coefficient ratio, the required color enhancement processing parameters can be determined quickly and accurately.

[0066] In one embodiment, the step of performing color enhancement processing on each to-be-enhanced region according to the enhancement preference information may include the following steps: obtaining a color correction matrix corresponding to the target enhancement coefficient ratio, and performing color enhancement processing according to the color correction matrix corresponding to each to-be-enhanced region.

[0067] That is, in this embodiment, the color enhancement processing parameter is a color correction matrix. Each color correction matrix corresponds to at least one area to be enhanced. The color correction matrix (CCM) can correct a low-saturation image into a high-saturation image. That is, the above correction can be understood as the process of mapping a low-saturation image into a high-saturation image through a preset mapping relationship. Moreover, based on different color correction matrices, the same color can be adjusted to different degrees. The color correction matrix can be generated, for example, using a three-dimensional lookup table (3D Look Up Table, 3D-LUT). Specifically, the source color space with low saturation can be divided into multiple grids, and the target color space with high saturation can also be divided into multiple grids, and a one-to-one mapping relationship between the grids can be established. That is, a grid in the source color space corresponds to a grid in the target color space. In this embodiment, the image to be processed can be understood as an image with low saturation. Therefore, the corresponding target enhancement coefficient ratio can be obtained through the user's enhancement preference information, and one or more corresponding color correction matrices can be determined according to preset rules and the target enhancement coefficient ratio, so as to perform personalized color enhancement processing through appropriate color correction matrices.

[0068] Specifically, as mentioned in the foregoing description, the same person may have different requirements for different hues. Therefore, at some target enhancement coefficients ratio, different color correction matrices may be adopted for different regions to be enhanced; and at some other target enhancement coefficients ratio, the same color correction matrix may be adopted for all regions to be enhanced. Therefore, in this embodiment, based on the above method of selecting the color correction matrix, different color enhancement processes can be performed on different regions, thereby improving the overall effect of the color enhancement process.

[0069] In one embodiment, the human eye's perception of the three primary colors can be integrated into the color calibration matrix to achieve more accurate color calibration. Specifically, a 24-color standard color card can be photographed at a certain color temperature first to obtain the RGB information of the 24 colors of the camera, and then a colorimeter can be used to measure and obtain the 24 groups of CIE1931 XYZ tristimulus values of the 24-color standard color card. Among them, the XYZ tristimulus values can be understood as being jointly obtained based on the RGB information and the human eye matching function. Therefore, based on the above data, a mapping relationship that can reflect the human eye's perception between the RGB information and the XYZ tristimulus values can be established, and this mapping relationship can be used as one of the factors for establishing the color calibration matrix, thereby achieving a more accurate color enhancement effect.

[0070] In one embodiment, before dividing the image to be processed into multiple regions to be enhanced according to the hue distribution of the image to be processed, it further includes obtaining the skin color region in the image to be processed and adjusting the color attributes of the skin color region to protect the color of the skin color region. The skin color region refers to the region in the image where human skin color exists. It can be understood that during the color enhancement process, the color attributes of each region will be adjusted, but excessive color enhancement may cause the distortion of the skin color and affect the user's viewing experience. Therefore, in this embodiment, by screening and protecting the skin color region, the problem of skin color distortion can be effectively avoided, thereby improving the display quality.

[0071] Optionally, the skin color region can be determined according to the current color space and the corresponding threshold conditions in this space. For example, if the current color space is the RGB color space, the skin color region can be determined by the following threshold conditions: R>95, G>40, B>20, where R>G, R>B, Max(R, G, B)-Min(R, G, B)>15 and Abs(R-G)>15. If the current color space is the HSV color space, the skin color region can be determined by the following threshold conditions: 7<H<20, 28<S<256 and 50<V<256. It can be understood that the above examples are only for illustrative purposes and are not used to limit the protection scope of this embodiment. In other embodiments, the color space can also be the YCrCb color space or the like and corresponding threshold conditions are set.

[0072] Figure 5 This is a fourth flow chart of an image processing method according to an embodiment, referring to Figure 5 In one embodiment, the image processing method includes steps 502 to 520. The specific implementation of steps 502 to 508 and steps 518 to 520 can refer to the above embodiment and will not be repeated here. That is, before the steps in the above embodiment divide the image to be processed into multiple areas to be enhanced, the method also includes steps 510 to 516 of this embodiment.

[0073] Step 502: Generate a candidate image set.

[0074] Step 504: Obtain the user's preference selection results for multiple images in each candidate image set.

[0075] Step 506: Obtain candidate enhancement coefficients corresponding to each candidate image set.

[0076] Step 508: Obtain a target enhancement coefficient ratio as enhancement preference information according to the multiple candidate enhancement coefficients.

[0077] Step 510: Acquire the image to be processed, and convert the image to be processed from the RGB color space to the target color space.

[0078] The target color space is the HSI color space or the HSV color space. Taking the HSV color space as an example, H (Hue) is the hue value, which refers to the full color attribute of the color. Each color corresponds to a different hue value, and the value range is 0° to 359°. S (Saturation) is the saturation value, which refers to the degree of dilution by white. The more dilution, the less saturated, and the closer it is to the center point. The value range is 0 to 1. V (Value) is the color brightness. The brighter the color, the higher the brightness, and vice versa. The value range is 0 to 1. The conversion relationship formula between the RGB color space and the HSV color space is as follows, where max is the maximum value of R, G, and B, and min is the minimum value of R, G, and B.

[0079]

[0080]

[0081] v=max

[0082] Step 512: Acquire the skin color area in the image to be processed.

[0083] It's understandable that the image being processed is currently in the HSV color space, meaning that the skin color region is determined based on the threshold conditions in the aforementioned HSV color space. Furthermore, based on the definitions of the H, S, and V components, it's clear that the components in the HSV color space are independent of each other. Therefore, compared to the RGB color space, the HSV color space is less susceptible to the effects of light. By converting the RGB color gamut to the HSV color gamut, the effects of light on the image can be effectively reduced, preventing misjudgment of skin color regions.

[0084] Step 514: Adjust the saturation and / or brightness of the skin color area.

[0085] Step 516 : Convert the image after adjusting the color attributes of the skin color area from the target color space to the RGB color space.

[0086] Step 518: Divide the image converted back into the RGB color space into a plurality of areas to be enhanced according to the hue.

[0087] Step 520 : performing corresponding color enhancement processing on each to-be-enhanced region according to the enhancement preference information, so as to adjust the color attribute of the to-be-processed image.

[0088] In this embodiment, the skin color area is defined in the HSV color space and protected. This can enhance the color saturation while preventing the skin color area from being over-enhanced, thereby maintaining the naturalness of the skin color.

[0089] In one embodiment, the color space conversion operation can be implemented using analog circuitry. Specifically, corresponding analog circuitry is built based on the color space conversion logic, thereby implementing the color space conversion using the analog circuitry. It is understood that processors only support operations on digital signals. Therefore, if a processor is used to implement the color space conversion operation, the analog signal must be converted to a digital signal for the operation. However, taking 256 grayscale as an example, two analog signals that are similar but actually different may be converted to digital signals at the same grayscale after digital-to-analog conversion, resulting in partial data loss. Furthermore, during the color space conversion, two colors that were different in the original color space may be mapped to two very similar colors in the new color space, resulting in partial color loss. Therefore, the multiple conversion operations described above can result in significant data loss in the digital signal. In this embodiment, implementing the color space conversion operation using analog circuitry can effectively avoid partial data loss, thereby improving the accuracy of the data processing process. Furthermore, the analog circuit operation process does not require clock control, resulting in relatively fast calculation speed.

[0090] In one embodiment, the step of dividing the image to be processed into a plurality of regions to be enhanced based on the tonal distribution of the image to be processed includes dividing the image to be processed into a plurality of regions to be enhanced and at least one transition region based on the tonal distribution of the image to be processed. Accordingly, the image processing method further includes smoothing the color attributes of the transition region based on the color attributes of the plurality of regions to be enhanced after the color enhancement process.

[0091] It is understandable that if the color correction matrix covers too many tonal areas, it may cause the color correction matrix to be too large, greatly increase the amount of calculation, and affect the speed of color enhancement. However, if the color correction matrix is ​​reduced, it will lead to poor enhancement effects on some colors. Therefore, the color correction matrix can usually only cover some key tonal areas (i.e., areas to be enhanced). In order to balance the relationship between calculation speed and accuracy, the color correction matrix usually only covers 24 tonal areas. Therefore, there are usually some areas in the image to be processed that do not have corresponding color correction matrices. In this embodiment, for the above-mentioned transition area, based on the result of color enhancement of the area to be enhanced, enhancement can be performed by smoothing, thereby achieving color enhancement of the entire image to be processed. Among them, the smoothing method can adopt any method with a smoothing effect such as interpolation, which is not limited in this embodiment.

[0092] Furthermore, when using the HSV color space for skin color protection, only the S and V components of the skin color area can be adjusted in the HSV color space, while the H component of the skin color area remains unchanged to ensure the accuracy of the skin color tone. After completing the skin color protection and converting back to the RGB color space, the smoothing of the skin color area and the smoothing of the transition area are performed in a single smoothing process, reducing the number of smoothing operations and thereby improving the speed of the image processing method.

[0093] In one embodiment, the image processing method further includes a platform verification step. Specifically, when the platform is adjusting the parameters of the color calibration matrix, the color information of the processed image can be detected by a color analyzer to determine whether the image generated by the color enhancement process meets the requirements. For example, it can be detected whether the boundary between the transition area and the area to be enhanced is sufficiently smoothed. Optionally, the platform verification step can be set before the skin color protection, or can be performed synchronously with the skin color protection. In this embodiment, by setting the platform verification step, the reliability and accuracy of the image processing algorithm can be tested before the display device leaves the factory, thereby improving the user experience.

[0094] In one embodiment, the image processing method further includes the following steps: performing color enhancement processing on the image to be processed according to the current preset color effect of the display screen to generate a first enhanced image, and fusing the first enhanced image and the second enhanced image according to a target enhancement coefficient ratio to generate a third enhanced image.

[0095] The preset color effect refers to the basic color effect that is set by default when the display device leaves the factory. That is, the preset color effect can be understood as a color effect unrelated to the user's enhancement preference information, such as a night effect or a dusk effect. The second enhanced image is an image generated by performing color enhancement processing on each area to be enhanced according to the enhancement preference information. Specifically, the basic effect can be referred to as 3DLut_CC, and the color enhancement effect performed according to the enhancement preference information can be referred to as 3DLut_ENIR. Through the steps of this embodiment, the two are fused to form the total enhancement effect 3DLut_OUT = 3DLut_CC + Delta0 + Ratio * Delta1. Here, Delta0 = 3DLutENIR0 - 3DLutCC, and Delta1 = 3DLutENIR1 - 3DLutENIR0. 3DLutENIR0 refers to the final effect at the weakest color enhancement, and 3DLutENIR1 refers to the final effect at the strongest color enhancement. Therefore, based on the above formula, the fusion of different enhancement effects can be achieved, that is, the fusion of the first and second enhanced images based on the target enhancement coefficient ratio is achieved.

[0096] In one of the embodiments, the image processing method further includes the following steps. When the target enhancement coefficient ratio is equal to the coefficient threshold, the target color gamut supported by the display screen is obtained. If the target color gamut is larger than the color gamut of the image after color enhancement processing, the color gamut of the image after color enhancement processing is expanded. For example, if the target color gamut natively supported by the display screen is 110% NTSC, and the color gamut range of 3DLut_CC is 90% NTSC, the color gamut can be expanded to 100% NTSC when the color enhancement effect is the strongest, so as to adjust in combination with the hardware of the display screen to achieve better color rendering effects.

[0097] In one embodiment, the display screen may also have an IR-drop adjustment function. Specifically, in a display screen with an active matrix organic light emitting diode (AMOLED) structure, when current is transmitted along the circuit traces, the impedance of the circuit traces causes a voltage drop. This voltage drop phenomenon can be called IR-drop. Therefore, the display driver chip can control the IR-drop of the display screen to be turned on or off to control the display effect. Specifically, when the IR-drop compensation function of the display screen is turned off, that is, when the display screen has IR-drop, the pixel brightness of the three colors R, G, and B satisfies the following relationship: R+G+B=xW. Where x is a fixed value approximately equal to 1.4, the specific value of this fixed value is determined by the circuit design of the display screen. Therefore, based on the above relationship, it can be seen that when the IR-drop compensation function of the display screen is turned off, the contrast of the display screen is the highest. When the IR-drop compensation function of the display screen is turned on, that is, when the display screen does not have IR-drop, the contrast of the display screen is the lowest, and the image after color enhancement processing lacks a sense of depth. Based on the above, it can be found that if the IR-drop compensation function is only set to two states, on and off, the display effect will be extreme and the display flexibility will be insufficient.

[0098] Therefore, in this embodiment, the contrast can be flexibly adjusted through the stepless adjustment function of IR-drop to improve the display quality. Optionally, before performing skin color protection, an instruction can be issued to turn on the stepless adjustment function of IR-drop. When the adjustment function is just turned on, the initialization state is that the IR-drop compensation function is turned off, that is, the contrast of the display screen is the largest, and after completing the steps of performing corresponding color enhancement processing on each area to be enhanced according to the enhancement preference information, the degree of IR-drop can be adjusted according to the enhanced image, thereby changing the contrast of the display screen. Specifically, the IR-drop adjustment in the image processing method can include the following steps. Obtain the average image level of the image after color enhancement processing, and adjust the brightness of each color channel according to the average image level to adjust the contrast of the image after color enhancement processing.

[0099] The average picture level (APL) refers to the average grayscale value of all pixels in the current color-enhanced image. To obtain the average picture level, you can first obtain the grayscale value of each pixel and the total number of pixels in the current color-enhanced image. Based on the grayscale value of each pixel and the total number of pixels, you can calculate the average grayscale value of the current color-enhanced image. For example, you can accumulate the grayscale values ​​of all pixels in the image and divide the accumulated value by the total number of pixels in the image to obtain the average grayscale value of the image.

[0100] The brightness of each color channel can be adjusted by adjusting the gamma value. Specifically, the human eye is sensitive to brightness changes in low-brightness environments and can perceive small brightness differences. However, in high-brightness environments, it is insensitive to brightness changes and can only distinguish large changes in brightness. This characteristic can be called the gamma characteristic. Therefore, if a uniform brightness change is required, the brightness displayed by the display needs to change non-uniformly to adapt to the gamma characteristics of the human eye. The parameter of this nonlinear relationship between brightness and grayscale is the gamma value. Therefore, after obtaining the gamma correction value of the image after the current color enhancement processing, the gamma correction curve corresponding to the gamma correction value can be drawn according to the gamma correction value. The gamma correction curve is the gamma correction curve of the image. According to the gamma correction curve, the target brightness corresponding to the pixels of different gray levels in the image can be obtained, and the current brightness of the pixels of different gray levels in the image can be adjusted to the target brightness, thereby realizing the adjustment of the brightness of the image, accurately displaying the brightness of the pixels of different gray levels in the image after the current color enhancement processing, making the brightness change consistent with the perception of the human eye, improving the color transparency and light and shadow relationship of the image, making the contrast of the image more intense and vivid, and optimizing the display effect of the display.

[0101] In this embodiment, by obtaining the average grayscale of the display image, the gamma correction value of the image can be obtained according to the average grayscale of the image. According to the gamma correction value of the image, the brightness of the image after the current color enhancement processing can be adjusted, thereby optimizing the display effect of the display while retaining the IR-drop characteristics of the display, making the contrast of the image after the color enhancement processing more vivid, and improving the layering of the image after the color enhancement processing.

[0102] In one embodiment, the image processing method further includes performing corresponding color enhancement processing on the image to be processed according to the current color mode and / or rendering intent of the display screen to generate a fourth enhanced image, and fusing the fourth enhanced image with the second enhanced image.

[0103] The color enhancement process can be achieved by calling the platform's preset color adjustment interface. Specifically, the mobile phone platform's preset color modes include natural color, enhanced effect, saturated color, and automatic adjustment. The preset rendering intents include perceptual constancy, relative color matching, absolute color matching, and saturation. The perceptual constancy rendering intent can be used, for example, in photographic images; the relative color matching rendering intent can be used, for example, in logo rendering; the absolute color matching rendering intent can be used, for example, in paper-like simulations; and the saturation rendering intent is commonly used, for example, in business charts and illustrations. It will be understood that the specific methods for color enhancement based on the color mode and rendering intent, as well as for fusing the two enhanced images, are preset by the platform and are not limited in this embodiment. It will be understood that the platform can make certain adjustments to the color of the processed image based on the preset methods. However, due to the platform's Gamma 2.2 protection and color gamut protection policies, the platform can only perform minor adjustments. Therefore, in this embodiment, the second enhanced image generated in the aforementioned embodiments can achieve a more flexible and accurate color enhancement effect.

[0104] Figure 6 This is a diagram showing the internal structure of a display device according to an embodiment. Figure 6 , the display device may include a software part and a hardware part. The hardware part includes a display screen for display, and the software part includes an application layer (APP), a framework layer (Framework), and a core layer (kernel). The framework layer can also be specifically divided into a Java part and a Native part. The Java part is used to connect with the application, but the Java language itself cannot access and operate the bottom layer of the operating system. Therefore, it is possible to interact with the bottom layer by setting up the Native part. Among them, the Java part can call the Native part through the Java Native Interface (JNI).

[0105] In this embodiment, the application layer interacts with the user to obtain the preference selection result containing the user's enhancement preference information, and then analyzes the result to obtain the target enhancement coefficient ratio that represents the user's enhancement preference information. The obtained target enhancement coefficient ratio is then transferred to the database and referred to as the Strength value. The database may also store other SystemUI and Setting information.

[0106] The framework layer monitors changes in the application layer's database information and retrieves the corresponding SettingProvider key and Strength value when the database information changes. The SettingProvider key is a button marker, used to identify which button on the phone was pressed, triggering subsequent software logic. After obtaining this data, it is transmitted in two ways.

[0107] Data is transmitted via the Android HIDL interface, reaching both the core layer path to issue a maximum IR-drop command to the display driver chip and the basic effects path to achieve personalized color enhancement. The 3DLut_ENH module performs multiple steps, including obtaining the skin tone region in the processed image and adjusting the color properties of the skin tone region to protect the color. The image is divided into multiple enhancement regions based on its tonal distribution, and each enhancement region is subjected to color enhancement processing based on enhancement preference information to adjust the color properties of the image. The 3DLut_IRC module compensates the color-enhanced image based on the average image level to improve the image's transparency by adjusting the degree of IR-drop. The 3DLut_ENIR module integrates and consolidates the effects of the 3DLut_ENH and 3DLut_IRC modules. The 3DLut_CC module is used to fuse the first and second enhanced images based on the target enhancement coefficient ratio to generate a third enhanced image. The first enhanced image is generated by color-enhancing the image to be processed according to the display's current preset color effect, and the second enhanced image is generated by color-enhancing the image based on the user's enhancement preferences. Another data stream is transmitted through the RenderIntent interface and reaches SurfaceFlinger and mobile_color_mode for color fine-tuning based on the color mode and rendering intent. After processing, the two image data streams are fused and transmitted to the Phone_color hardware in the core layer.

[0108] The Phone_color hardware in the core layer obtains the data and writes it into the hardware color engine register of the display screen, thereby realizing the display at the hardware layer.

[0109] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0110] Based on the same inventive concept, embodiments of the present application also provide an image processing device for implementing the aforementioned image processing method. The solution to the problem provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more of the following image processing device embodiments can be found in the above-mentioned limitations of the image processing method and will not be further elaborated here. The image processing device includes a preference acquisition module, a region division module, and an enhancement processing module.

[0111] The preference acquisition module is used to obtain the user's enhancement preference information. The region division module is used to obtain the image to be processed and divide the image to be processed into multiple regions to be enhanced based on the tonal distribution of the image to be processed. The enhancement processing module is used to perform color enhancement processing on each region to be enhanced based on the enhancement preference information to adjust the color properties of the image to be processed.

[0112] Each module in the above-mentioned image processing device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in an electronic device in the form of hardware, or can be stored in a memory in the electronic device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0113] The present application also provides a display device comprising a display screen and a processor connected thereto. The processor is configured to obtain enhancement preference information from a user, obtain an image to be processed, and divide the image into a plurality of regions to be enhanced based on the tonal distribution of the image to be processed, perform color enhancement processing on each region to be enhanced based on the enhancement preference information to adjust the color attributes of the image to be processed, and transmit the image with the adjusted color attributes to the display screen for display.

[0114] In one embodiment, an electronic device is provided, which may be a display device. Figure 7This is an internal structure diagram of an electronic device of an embodiment. The electronic device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external display device in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, an image processing method is implemented. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the electronic device housing, or an external keyboard, touchpad or mouse.

[0115] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0116] In one embodiment, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0117] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0118] In one embodiment, a computer program product is provided, including a computer program, which implements the steps of the above method embodiments when executed by a processor.

[0119] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be a general-purpose processor, a central processing unit, an image processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to these.

[0120] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above 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.

[0121] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. An image processing method, characterized in that: The method comprises: generating a candidate image set, the candidate image set including a plurality of images, the plurality of images in the same candidate image set corresponding to different color enhancement processing parameters; the plurality of candidate image sets being multiple, the plurality of candidate image sets including at least a first candidate image set and a second candidate image set, the first candidate image set being used to obtain a user's preference for blue-green tones, and the second candidate image set being used to obtain a user's preference for red-green-blue tones; the color enhancement processing parameters corresponding to the plurality of images in the (n+1)th candidate image set being determined by the preference selection result of the (n)th candidate image set, where n is a positive integer; Obtaining a user's preference selection results for a plurality of images in each candidate image set, and obtaining enhanced preference information according to the preference selection results; Acquire an image to be processed, and divide the image to be processed into a plurality of areas to be enhanced according to the tone distribution of the image to be processed; According to the enhancement preference information, corresponding color enhancement processing is performed on each of the to-be-enhanced areas to adjust the color attribute of the to-be-processed image.

2. The image processing method according to claim 1, wherein: The number of the candidate image sets is two, and generating the candidate image sets includes: generating a first candidate image set according to the first initial image with a blue-green tone and the first parameter; A second candidate image set is generated according to the second initial image with red, green and blue tones and the second parameters.

3. The image processing method according to claim 2, wherein: Generating a second candidate image set according to the second initial image with red, green and blue tones and the second parameter comprises: determining the second parameter of the color enhancement process according to the user's preference selection result for the plurality of images in the first candidate image set; The second candidate image set is generated according to the determined second parameter and the second initial image with red, green and blue tones.

4. The image processing method according to claim 1, wherein: The plurality of images in the candidate image set are respectively configured with a plurality of corresponding image enhancement coefficients, and obtaining the enhancement preference information according to the preference selection result includes: respectively obtaining a candidate enhancement coefficient corresponding to each candidate image set, wherein the candidate enhancement coefficient is the image enhancement coefficient corresponding to an image selected by a user from the candidate image set; A target enhancement coefficient is obtained according to the plurality of candidate enhancement coefficients as the enhancement preference information.

5. The image processing method according to claim 4, characterized in that Also includes: If a coefficient adjustment signal acting on a designated trigger area of ​​the interface is received, the target enhancement coefficient is updated according to the coefficient adjustment signal.

6. The image processing method according to claim 4, wherein: The performing corresponding color enhancement processing on each of the to-be-enhanced areas according to the enhancement preference information includes: Obtaining a color correction matrix corresponding to the target enhancement coefficient, each of the color correction matrices corresponding to at least one of the regions to be enhanced; Color enhancement processing is performed according to the color correction matrix corresponding to each of the areas to be enhanced.

7. The image processing method according to claim 6, characterized in that: The step of dividing the image to be processed into a plurality of areas to be enhanced according to the tone distribution of the image to be processed comprises: Dividing the image to be processed into a plurality of to-be-enhanced areas and at least one transition area according to the tone distribution of the image to be processed; The image processing method further includes: The color attributes of the transition area are smoothed according to the color attributes of the plurality of areas to be enhanced after the color enhancement process.

8. The image processing method according to claim 4, wherein: Also includes: Performing color enhancement processing on the image to be processed according to a current preset color effect of the display screen to generate a first enhanced image; The first enhanced image and the second enhanced image are fused according to the target enhancement coefficient to generate a third enhanced image, wherein the second enhanced image is an image generated by performing corresponding color enhancement processing on each of the to-be-enhanced areas according to the enhancement preference information.

9. The image processing method according to claim 4, wherein: Also includes: When the target enhancement coefficient is equal to the coefficient threshold, obtaining a target color gamut supported by the display screen; If the target color gamut is larger than the color gamut of the image after color enhancement processing, the color gamut of the image after color enhancement processing is expanded.

10. The image processing method according to claim 1, wherein: Before dividing the image to be processed into a plurality of areas to be enhanced according to the tone distribution of the image to be processed, the method further includes: Acquire a skin color area in the image to be processed; The color attribute of the skin color area is adjusted to perform color protection on the skin color area.

11. The image processing method according to claim 10, wherein: Before obtaining the skin color area in the image to be processed, the method further includes: converting the image to be processed from the RGB color space to a target color space, wherein the target color space is an HSI color space or an HSV color space; After adjusting the color attribute of the skin color area, the method further includes: converting the image after the color attribute of the skin color area is adjusted from the target color space to the RGB color space.

12. The image processing method according to claim 1, wherein: Also includes: Obtaining the average image level of the color enhanced image; The brightness of each color channel is adjusted according to the average image level to adjust the contrast of the image after color enhancement processing.

13. The image processing method according to claim 1, wherein: Also includes: performing corresponding color enhancement processing on the image to be processed according to the current color mode and / or rendering intent of the display screen to generate a fourth enhanced image; The fourth enhanced image and the second enhanced image are fused to generate a target display image, wherein the second enhanced image is an image generated by performing corresponding color enhancement processing on each of the to-be-enhanced areas according to the enhancement preference information.

14. An image processing device, characterized in that: The device comprises: a preference acquisition module configured to generate a candidate image set, wherein the candidate image set includes multiple images, and the multiple images in the same candidate image set have different corresponding color enhancement processing parameters; the number of the candidate image sets is multiple, and the multiple candidate image sets include at least a first candidate image set and a second candidate image set, the first candidate image set being used to acquire the user's preference for blue-green tones, and the second candidate image set being used to acquire the user's preference for red-green-blue tones; acquiring the user's preference selection results for the multiple images in each of the candidate image sets, and acquiring enhancement preference information based on the preference selection results; the color enhancement processing parameters corresponding to the multiple images in the (n+1)th candidate image set are determined by the preference selection results of the (n)th candidate image set, where n is a positive integer; A region division module is used to obtain an image to be processed and divide the image to be processed into a plurality of regions to be enhanced according to the tone distribution of the image to be processed; The enhancement processing module is used to perform corresponding color enhancement processing on each of the to-be-enhanced areas according to the enhancement preference information, so as to adjust the color attributes of the to-be-processed image.

15. A display device, characterized in that: include: Display screen; a processor, connected to the display screen, configured to generate an alternative image set, the alternative image set including a plurality of images, the plurality of images in the same alternative image set corresponding to different color enhancement processing parameters; the number of the alternative image sets being multiple, the plurality of alternative image sets including at least a first alternative image set and a second alternative image set, the first alternative image set being used to obtain a user's preference for blue-green tones, and the second alternative image set being used to obtain a user's preference for red-green-blue tones; The color enhancement processing parameters corresponding to the multiple images in the n+1th candidate image set are determined by the preference selection result of the nth candidate image set, where n is a positive integer; Obtaining a user's preference selection results for a plurality of images in each candidate image set, and obtaining enhanced preference information according to the preference selection results; Acquire an image to be processed, and divide the image to be processed into a plurality of areas to be enhanced according to the tone distribution of the image to be processed; According to the enhancement preference information, corresponding color enhancement processing is performed on each of the to-be-enhanced areas to adjust the color attributes of the to-be-processed image; and the image after the color attributes are adjusted is sent to the display screen for display.

16. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 13 are implemented.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 13 are implemented.

Images