An image processing system for display quality adjustment

Abstract

The application discloses an image processing system for display quality adjustment, acquires image information to be processed; determines first data matching information based on the image information to be processed; and processes the image information to be processed based on the first data matching information to obtain first target image information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, and particularly relates to an image processing system for display quality adjustment. BACKGROUND

[0002] With the development of display technology, people can watch videos, images and the like through various display devices such as televisions, displays, mobile devices and the like. In order to obtain a display picture with better display effect, color adjustment needs to be performed on the videos, images and the like to be displayed. However, the existing color adjustment method has the problem of poor color adjustment effect. SUMMARY

[0003] Embodiments of the present application provide an image processing system for display quality adjustment.

[0004] In a first aspect, the present application provides a method, comprising:

[0005] obtaining image information to be processed;

[0006] determining first data matching information based on the image information to be processed;

[0007] processing the image information to be processed based on the first data matching information to obtain first target image information.

[0008] In a second aspect, the present application provides a system, comprising:

[0009] an information obtaining module configured to obtain image information to be processed;

[0010] an information determining module configured to determine first data matching information based on the image information to be processed;

[0011] an image processing module configured to process the image information to be processed based on the first data matching information to obtain first target image information.

[0012] In a third aspect, the present application further provides a device, comprising:

[0013] one or more processors;

[0014] a memory; and

[0015] one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the method of any one of the first aspect.

[0016] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is loaded by a processor to execute the steps in the method of any one of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description only represent some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0018] Figure 1 is a scene schematic diagram of an image processing system provided by an embodiment of the present application;

[0019] Figure 2 is an embodiment flowchart of an image processing method provided by an embodiment of the present application;

[0020] Figure 3 is a specific embodiment flowchart of determining first data matching information provided by an embodiment of the present application;

[0021] Figure 4 is a specific embodiment flowchart of processing the to-be-processed image information provided by an embodiment of the present application;

[0022] Figure 5 is another embodiment flowchart of an image processing method provided by an embodiment of the present application;

[0023] Figure 6 is a principle block diagram of an image processing system provided by an embodiment of the present application;

[0024] Figure 7 is an embodiment structure schematic diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments only represent some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0026] In the description of the present application, it should be understood that the terms “first”, “second”, “third” and the like are only used for description purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features limited by “first”, “second”, “third” and the like can explicitly or implicitly include one or more features.

[0027] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0028] It should be noted that since the method in this application embodiment is executed in a computer device, the processing objects of each computer device exist in the form of data or information, such as time, which is essentially time information. It is understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data that exist so that the computer device can process them. Specific details will not be elaborated here.

[0029] This application provides an image processing method and system for adjusting display image quality, which will be described in detail below.

[0030] Please see Figure 1 , Figure 1 This is a schematic diagram of a scene of the image processing system provided in an embodiment of this application. The image processing system may include a computer device 100, and the computer device 100 integrates the image processing system, such as... Figure 1 Computer equipment in the country.

[0031] In this embodiment, the computer device 100 is mainly used to acquire image information to be processed; determine first data matching information based on the image information to be processed; process the image information to be processed based on the first data matching information to obtain first target image information; and adjust the color of the image information to be processed according to the image content contained in the image information to be processed, thereby improving the naturalness and accuracy of the color adjustment.

[0032] In this embodiment, the computer device 100 can be a standalone server, a server network, or a server cluster. For example, the computer device 100 described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0033] It is understood that the computer device 100 used in the embodiments of this application can be a device that includes both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device may include: cellular or other communication devices having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the computer device 100 may be a desktop terminal or a mobile terminal, and may also be one of a mobile phone, tablet computer, laptop computer, etc.

[0034] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include those that are more specific to this application. Figure 1 The number of computer devices shown is more or less, for example Figure 1 Only one computer device is shown in the image. It is understood that the image processing system may also include one or more other services, which are not limited here.

[0035] In addition, such as Figure 1 As shown, the image processing system may also include a memory 200 for storing data, such as image information, such as first image information, second image information, etc., and location information, such as first region location information, second region location information, etc.

[0036] It should be noted that, Figure 1 The schematic diagram of the image processing system shown is merely an example. The image processing system and scenario described in this application are for the purpose of more clearly illustrating the technical solutions of this application and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of image processing systems and the emergence of new business scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0037] like Figure 2 The diagram shown is a flowchart of an embodiment of the image processing method in this application. The image processing method may include the following steps S201 to S203, as detailed below:

[0038] S201. Obtain the image information to be processed.

[0039] In this embodiment, the image information to be processed is information acquired by a computer device that requires image processing. Optionally, the image information to be processed can be image information acquired by the imaging module configured on the computer device itself, image information captured by a high-definition camera, or image information acquired by the imaging module of other computer devices through means such as network, Bluetooth, and infrared. This embodiment does not limit the scope of the application. For example, when the image processing method of this application is applied to a smartphone, the smartphone can directly acquire the image information to be processed through its own imaging module. When the image processing method of this application is applied to a server, the server can acquire the image information to be processed through the imaging module of the smartphone and obtain the image information to be processed from the smartphone through means such as network, Bluetooth, and infrared.

[0040] Furthermore, the image information to be processed can be static image data or dynamic video data. For example, the image information to be processed can be the webpage image data currently being viewed by the user, or the video data of the TV program currently being watched by the user. This embodiment does not limit this.

[0041] S202. Based on the image information to be processed, determine the first data matching information.

[0042] In this embodiment, the first data matching information is determined based on the image information to be processed. The data matching information represents the matching / correspondence between data points, which can be pixel values ​​or other data representations. Optionally, the first data matching information is used to represent the matching / correspondence between pixel values. The pixel in the pixel value refers to the pixel of the digital image. Pixels are the basic units that constitute a digital image; each pixel has a specific location and an assigned color value. The arrangement and combination of pixels determine the detail and clarity of the image. This embodiment processes the image information to be processed based on the first data matching information, which can adjust the pixel values ​​of the image information to be processed, thereby making the colors of the image information to be processed more accurate and realistic.

[0043] In one specific embodiment, the first data matching information can be a three-dimensional color lookup table (3DLUT). 3DLUT can achieve more accurate and flexible color conversion through non-linear color correction and adjustment, making the color conversion more natural and accurate.

[0044] Furthermore, the first data matching information varies depending on the object category information corresponding to the image information to be processed. The object category information corresponding to the image information to be processed is the category to which the objects contained in the image information belong. For example, if the object category information corresponding to the image information to be processed includes background and sky, then the first data matching information includes the data matching information corresponding to the background and the data matching information corresponding to the sky; if the object category information corresponding to the image information to be processed includes background, sky, and plants, then the first data matching information includes the data matching information corresponding to the background, the data matching information corresponding to the sky, and the data matching information corresponding to the plants; if the object category information corresponding to the image information to be processed includes background, sky, plants, and human skin color, then the first data matching information includes the data matching information corresponding to the background, the data matching information corresponding to the sky, the data matching information corresponding to the plants, and the data matching information corresponding to human skin color.

[0045] In a specific implementation method, refer to Figure 3 As shown, the step S202 above, which determines the first data matching information based on the image information to be processed, may include steps S301 to S302, as follows:

[0046] S301. Perform image segmentation processing on the image information to be processed to obtain image segmentation result information; the image segmentation result information includes at least one object category information.

[0047] In this embodiment, the image segmentation result information is the segmentation result information obtained by performing image segmentation processing on the image information to be processed. The image segmentation result information includes at least one object category information and the third region location information corresponding to each object category information. Image segmentation is a technique and process of dividing an image into several specific regions with unique properties and extracting objects of interest. By performing image segmentation on the image information to be processed, the image information to be processed can be divided into multiple regions, each region consisting of the same type of object. The third region location information corresponding to each object category information represents the region location of the object corresponding to each object category information in the image information to be processed. The object category information represents the category to which the objects contained in the region corresponding to the third region location information belong. The object category can be divided by manually defining one or more objects into the same object category, or it can be determined automatically by machine learning or deep learning models. For example, pre-trained image segmentation models (such as Mask R-CNN, DeepLab, etc.) can be used to automatically identify and classify objects in images. When using machine learning methods, the model can be trained based on large-scale labeled data to enable it to identify object categories in specific scenes or applications and generate corresponding region location information. For example, if the object category in the area corresponding to the third location information A is sky, the object category in the area corresponding to the third location information B is green plants, and the object category in the area corresponding to the third location information C is human skin color, then the object category information corresponding to the third location information A, the third location information B, and the third location information C is sky, green plants, and human skin color, respectively.

[0048] In one specific embodiment, the steps of performing image segmentation processing on the image information to be processed to obtain image segmentation result information specifically include: inputting the image information to be processed into an image segmentation model, performing image segmentation processing on the image information to be processed through the image segmentation model, and obtaining image segmentation result information.

[0049] In one specific embodiment, the image segmentation model is obtained by training a preset network model using a training sample set. The training sample set includes first image information, sample category information corresponding to the first image information, and sample location information corresponding to the sample category information. Before the above-mentioned step of performing image segmentation processing on the image information to be processed to obtain image segmentation result information, the method includes: inputting the first image information into the preset network model, outputting the predicted category information corresponding to the first image information and the predicted location information corresponding to the predicted category information through the preset network model; and training the preset network model based on the predicted category information, predicted location information, sample category information, and sample location information to obtain the image segmentation model.

[0050] Further, the step of training the preset network model based on predicted category information, predicted location information, sample category information, and sample location information to obtain the image segmentation model includes: determining the loss value based on the predicted category information, predicted location information, sample category information, sample location information, and the loss function of the preset network model; if the loss value does not meet the first condition, correcting the model parameters of the preset network model based on a preset parameter learning rate, and continuing to execute the steps of inputting the first image information into the preset network model and outputting the predicted category information and the predicted location information corresponding to the first image information through the preset network model, until the loss value meets the first condition. Wherein, the loss value meeting the first condition can be defined as the loss value being less than a first threshold or the difference between two consecutive loss values ​​being less than a second threshold.

[0051] In this embodiment, an image segmentation model is obtained by training a preset network model using a training sample set. Then, image segmentation processing is performed on the image information to be processed based on the image segmentation model. The image segmentation model can automatically identify and classify objects in the image information to be processed, thereby improving the accuracy of the obtained image segmentation results and the efficiency of image segmentation processing.

[0052] S302. Determine first data matching information based on at least one object category information.

[0053] In one specific embodiment, the step of determining the first data matching information based on at least one object category information specifically includes: extracting features from the image information to be processed to obtain first image feature information; and determining the first data matching information based on at least one object category information, the first image feature information, and first association information. The first association information represents the correspondence between the object category information, the image feature information, and the data matching information.

[0054] In this embodiment, the first image feature information is a key feature extracted from the illumination data of the image information to be processed. For example, the first image feature information may include one or more of the following: brightness, contrast, saturation, chroma, sharpness, and color temperature value of the image information to be processed. In a specific embodiment, the step of extracting features from the image information to be processed to obtain the first image feature information specifically includes: performing image analysis processing on the image information to be processed to obtain the illumination data of the image information to be processed; and performing feature extraction on the illumination data to obtain the first image feature information.

[0055] In this embodiment, illumination data refers to data used in image processing and computer graphics to represent illumination effects. Illumination data is used to simulate illumination effects in the real world. Image illumination data typically includes brightness, contrast, saturation, chroma, sharpness, and color temperature. Brightness represents the grayscale value of the image, contrast represents different brightness levels in bright and dark areas of the image, saturation represents the vividness of the image colors, chroma represents the hue and saturation of the image colors, sharpness represents the clarity of the image edges and the contrast of details, and color temperature is a parameter representing the warmth or coolness of the light emitted by the light source when the image is captured. By combining illumination data to determine first data matching information, and then processing the image information to be processed based on the first data matching information, the processed image information to be processed can be made closer to the lighting environment of the real world, thus improving the image processing effect of the image information to be processed.

[0056] In one specific embodiment, the Blinn-Phong lighting model can be used to perform image analysis and processing on the image information to be processed to obtain the lighting data of the image information to be processed. Alternatively, the Image-Based Lighting (IBL) algorithm can be used to perform image analysis and processing on the image information to be processed to obtain the lighting data of the image information to be processed. Or, the Physically Based Rendering (PBR) algorithm can be used to perform image analysis and processing on the image information to be processed to obtain the lighting data of the image information to be processed. This embodiment does not limit the scope of the method.

[0057] In one specific embodiment, the first association information is obtained through the following steps: obtaining first image information; determining data matching information corresponding to each object category information based on the first image information; and determining the first association information based on the data matching information corresponding to each object category information and the second image feature information corresponding to the first image information.

[0058] In this embodiment, the first image information is pre-collected image data containing information on various object categories. For example, the first image information is pre-collected image data containing sky, green plants, and human skin tones. The second image feature information is key features extracted from the illumination data of the first image information. For example, the second image feature information may include the contrast and color temperature value of the first image information. The process for determining the second image feature information is the same as the process for determining the first image feature information, and can be referred to the aforementioned process for determining the first image feature information. To avoid repetition, this embodiment will not repeat it here.

[0059] In one specific embodiment, the object category information includes first object category information. For example, the object category information includes sky, green plants, human body, and background, and the first object category information is background. The steps described above for determining the data matching information corresponding to each object category information based on the first image information specifically include: inputting the first image information into an image processing model, outputting the first color information of the first image information through the image processing model; and determining the data matching information corresponding to the first object category information based on the original color information, the first color information, and the first region location information of the first image information.

[0060] In this embodiment, the original color information is the color data corresponding to the first image information input to the image processing model. The first color information is the color data corresponding to the first image information after processing by the image processing model. Color data refers to the set of grayscale values ​​or color values ​​of each pixel represented by numerical values. For example, when the image is a grayscale image, the color data is the set of grayscale values ​​of all pixels in the grayscale image. When the image is an RGB image, the color data is the set of RGB values ​​of all pixels in the RGB image.

[0061] Furthermore, the image processing model is a model used to adjust the colors of an image by simulating the human eye's color perception process. By adjusting the first image information through the image processing model, the first image information can be made closer to the true colors observed by the human eye. The image processing model can be built based on the CAM16 color appearance model, the CIECAM02 color appearance model, or the iCAM color appearance model; this embodiment does not impose any limitations.

[0062] Furthermore, the first region location information represents the region location corresponding to the first object category information in the first image information. For example, if the first object category information is the background, the first region location information represents the region location corresponding to the background in the first image information. Based on the original color information, the first color information, and the first region location information of the first image information, the original color information and the first color information of the image region corresponding to the first object category information can be determined. Based on the original color information and the first color information of the image region corresponding to the first object category information, the data matching information corresponding to the first object category information can be determined.

[0063] In one specific embodiment, each object category information includes at least one second object category information. For example, each object category information includes sky, greenery, human body, and background, the first object category information is background, and at least one second object category information includes sky, greenery, and human body. The steps described above for determining the data matching information corresponding to each object category information based on the first image information specifically include: performing color correction processing on the first image information to obtain second image information; performing color conversion processing on the second image information based on the target color information corresponding to each second object category information to obtain second color information corresponding to each second object category information; and determining the data matching information corresponding to each second object category information based on the original color information, the second color information, and the second region location information of the first image information. The second region location information represents the region location corresponding to each second object category information in the first image information.

[0064] In this embodiment, the target color information corresponding to each second object category is the standard color information corresponding to each second object category determined based on user preferences. The target color information corresponding to each second object category can be manually selected by the user, or it can be determined by analyzing the user's color preferences using computer equipment; this embodiment does not impose any limitations. This embodiment determines the target color information based on the user's color preferences and determines the data matching information based on the target color information, which makes the colors of the adjusted image information to be processed more consistent with the user's visual expectations, thereby further improving the image processing effect of the image information to be processed. For example, the user can pre-select a set of standard colors as the target color information corresponding to the sky, green plants, and human skin tone, respectively. By performing color conversion processing on the second image information based on the target color information corresponding to the sky, green plants, and human skin tone, the second color information corresponding to the sky, green plants, and human skin tone can be obtained.

[0065] In one specific embodiment, color correction processing is a method of reducing or eliminating color deviations caused by shooting or displaying during the process of adjusting color values ​​in an image or video, thereby restoring or enhancing the realism and visual effect of the image. Optionally, a color constancy algorithm can be used to perform color correction processing on the first image information. The color constancy algorithm estimates the color temperature information of the first image information by analyzing the lighting conditions and color distribution in the first image information, and performs color correction on the first image information based on the color temperature information, so that the image can maintain a consistent visual effect under different lighting conditions. The color constancy algorithm can be the Gray-World algorithm, the Max-RGB algorithm, or the Shades-of-Gray algorithm; this embodiment does not limit the specific algorithm used.

[0066] In one specific embodiment, the color conversion process is the process of modifying the feature representation corresponding to the second image information to the feature representation corresponding to the second color information. When performing color conversion processing on the second image information based on the target color information corresponding to each second object category information, a histogram matching algorithm can be used to perform color conversion processing on the second image information, a color space conversion algorithm can be used to perform color conversion processing on the second image information, or an adaptive histogram equalization (AHE) algorithm can be used to perform color conversion processing on the second image information. This embodiment does not limit the specific method.

[0067] In another specific embodiment, the step of determining the first data matching information based on at least one object category information specifically includes: determining the first data matching information based on at least one object category information and second association information. The second association information represents the correspondence between the object category information and the data matching information.

[0068] In this embodiment, the second association information is obtained through the following steps: obtaining first image information; determining data matching information corresponding to each object category information based on the first image information; and determining the second association information based on the data matching information corresponding to each object category information. The step of determining the data matching information corresponding to each object category information based on the first image information has been discussed in the preceding steps and will not be repeated here to avoid repetition.

[0069] S203. Based on the first data matching information, process the image information to be processed to obtain the first target image information.

[0070] In this embodiment, processing the image information to be processed refers to adjusting the image data of the image information itself, that is, adjusting the pixel values ​​of the pixels in the digital image. For example, if the image information to be processed is RGB image data, and the pixel values ​​of the pixels in the three RGB channels are R1, G1, and B1 respectively, processing the image information to be processed means adjusting the pixel values ​​of the pixels in the image information to be processed from R1, G1, and B1 to R2, G2, and B2. The first target image information is the processed image information to be processed. In this embodiment, the first data matching information is determined based on the image information to be processed, and the image information to be processed is processed based on the first data matching information. Color adjustment can be performed on the image information to be processed according to the image content contained in the image information to be processed, thereby improving the naturalness and accuracy of color adjustment.

[0071] Furthermore, the first data matching information includes data matching information corresponding to at least one object category information. That is, the first data matching information may include only data matching information corresponding to one object category information, or it may include data matching information corresponding to at least two object category information. In a specific embodiment, when the first data matching information includes only data matching information corresponding to one object category information, the above-mentioned step of processing the image information to be processed based on the first data matching information to obtain the first target image information specifically includes: performing mapping processing on the image information to be processed based on the first data matching information to obtain the first target image information.

[0072] In this embodiment of the application, mapping processing refers to mapping each pixel value of the image information to be processed to a predefined output value, thereby adjusting the color of the digital image. By performing mapping processing on the image information to be processed, the color of the image information to be processed can be changed, ensuring the consistency of the same image displayed on different devices.

[0073] In another specific embodiment, the first data matching information includes data matching information corresponding to at least two object category information, as referred to Figure 4 As shown, step S203 above, which processes the image information to be processed based on the first data matching information to obtain the first target image information, may include steps S401 to S402, as follows:

[0074] S401. Based on the data matching information corresponding to each object category information, the image information to be processed is mapped to obtain the third image information corresponding to each object category information.

[0075] In this embodiment, the third image information is the image information to be processed after mapping the data matching information corresponding to each object category information. For example, by mapping the image information to be processed based on the data matching information corresponding to the sky, green plants, human skin color, and background, respectively, the third image information corresponding to the sky, green plants, human skin color, and background can be obtained.

[0076] S402. Based on the third image information corresponding to each object category information and the third region location information corresponding to each object category information, determine the first target image information.

[0077] In one specific embodiment, the image segmentation result information in the aforementioned step S301 further includes third region location information corresponding to each object category information. The third region location information represents the region location corresponding to each object category information in the image information to be processed. After determining the third image information corresponding to each object category information, the third image information corresponding to each object category information can be obtained from the image segmentation result information, and the first target image information can be determined based on the third image information corresponding to each object category information and the third region location information corresponding to each object category information.

[0078] In one specific embodiment, the step of determining the first target image information based on the third image information corresponding to each object category information and the third region location information corresponding to each object category information specifically includes: performing binarization processing on the image information to be processed based on the third region location information corresponding to each object category information to obtain the fourth image information corresponding to each object category information; performing fusion processing on the third image information and the fourth image information to obtain the fifth image information corresponding to each object category information; and performing fusion processing on the fifth image information to obtain the first target image information.

[0079] In this embodiment, the fourth image information is a binarized image obtained by binarizing the image information to be processed based on the third region location information. In the fourth image information, the pixel value of the pixel in the region corresponding to the third region location information is a first target value, and the pixel values ​​of the pixels in other regions of the fourth image information besides the region corresponding to the third region location information are second target values. For example, the pixel value of the pixel in the region corresponding to the third region location information in the fourth image information is 1, and the pixel value of the pixels in other regions of the fourth image information besides the region corresponding to the third region location information is 0.

[0080] In this embodiment, fusion processing refers to the process of fusing images or image sets from multiple sources into a single image to obtain a richer and more informative image result. This process is typically used to enhance image quality, compensate for defects between different images, and improve visual effects. In a specific embodiment, the step of fusing the third and fourth image information to obtain the fifth image information corresponding to each object category information specifically includes: performing pixel multiplication on the third and fourth image information to obtain the fifth image information corresponding to each object category information. The step of fusing the fifth image information to obtain the first target image information specifically includes: performing pixel addition on the fifth image information corresponding to all object category information to obtain the first target image information. This embodiment, by performing pixel multiplication on the third and fourth image information and then pixel addition on the fifth image information, allows the region corresponding to each object category information in the image information to be processed to be mapped according to the data matching information corresponding to that object category information, thereby improving the naturalness and accuracy of color adjustment.

[0081] In a specific implementation method, refer to Figure 5 As shown, after processing the image information to be processed based on the first data matching information to obtain the first target image information in step S203 above, steps S501 to S503 may be included, as follows:

[0082] S501. Perform region localization on the first target image information to obtain at least one region information to be processed; each region information to be processed corresponds to at least two data matching information.

[0083] In one specific embodiment, the first target image information corresponds to at least two object category information. Different object category information has a transition region in the first target image information. The region information to be processed is the region location information corresponding to the transition region of different object category information in the first target image information. For example, the first target image information includes sky and plants, and the region information to be processed is the region location information of the transition region corresponding to sky and plants.

[0084] Furthermore, each area information to be processed corresponds to at least two data matching information. These at least two data matching information are data matching information for the object category information corresponding to each area information to be processed. For example, if the area information to be processed is the area location information of the transition area corresponding to the sky and plants, then the data matching information corresponding to the area information to be processed includes the data matching information corresponding to the sky and the data matching information corresponding to the plants.

[0085] S502. Perform fusion processing on the data matching information corresponding to each area information to be processed to obtain the second data matching information corresponding to each area information to be processed.

[0086] In one specific embodiment, the step of fusing the data matching information corresponding to each region information to obtain the second data matching information corresponding to each region information to be processed specifically includes: performing weighted summation on the data matching information corresponding to each region information to obtain the second data matching information corresponding to each region information to be processed.

[0087] Optionally, the process of determining the second data matching information can be represented as follows: Where D represents the second data matching information, D i γ represents the matching information of the i-th data. i γ represents the weight corresponding to the matching information of the i-th data. i It can be configured according to actual needs.

[0088] S503. Based on the second data matching information and the information of the region to be processed, the first target image information is mapped to obtain the second target image information.

[0089] In this embodiment, the second target image information is the first target image information after color adjustment. In this embodiment, the data matching information corresponding to each region to be processed is fused to obtain the second data matching information corresponding to each region to be processed. Based on the second data matching information and the region to be processed, the first target image information is mapped to make the color transition of the transition area in the first target image information smooth and improve the image processing effect of the image information to be processed.

[0090] In summary, the image processing method provided in this embodiment determines first data matching information based on the image information to be processed, and processes the image information to be processed based on the first data matching information to obtain first target image information. It can adjust the color of the image information to be processed according to the image content contained therein, improving the naturalness and accuracy of the color adjustment. Furthermore, it extracts features from the image information to be processed to obtain first image feature information, and determines the first data matching information based on at least one object category information, the first image feature information, and first association information. This makes the processed image information to be processed more closely resemble the lighting environment of the real world, further improving the image processing effect of the image information to be processed. Even further, it adjusts the target color information based on the target color information corresponding to each second object category information. The first image information undergoes color conversion processing to obtain the second color information corresponding to each second object category. Based on the original color information, second color information, and second region position information of the first image information, the data matching information corresponding to each second object category is determined. This makes the color of the adjusted image information to be processed more in line with the user's visual expectations, further improving the image processing effect of the image information to be processed. Furthermore, the data matching information corresponding to each region information to be processed is fused to obtain the second data matching information corresponding to each region information to be processed. Based on the second data matching information and the region information to be processed, the first target image information is mapped, which can make the color transition of the transition region in the first target image information smooth, improving the image processing effect of the image information to be processed.

[0091] To better implement the image processing method in the embodiments of this application, an image processing system is also provided in the embodiments of this application, such as... Figure 6 As shown, the image processing system 600 includes:

[0092] Information acquisition module 610 is used to acquire image information to be processed;

[0093] The information determination module 620 is used to determine first data matching information based on the image information to be processed;

[0094] The image processing module 630 is used to process the image information to be processed based on the first data matching information to obtain the first target image information.

[0095] In this embodiment, first data matching information is determined based on the image information to be processed, and then the image information to be processed is processed based on the first data matching information to obtain first target image information. The color of the image information to be processed can be adjusted according to the image content contained in the image information to be processed, thereby improving the naturalness and accuracy of the color adjustment.

[0096] In some embodiments of this application, the information determination module 620 determines first data matching information based on the image information to be processed, including:

[0097] Image segmentation processing is performed on the image information to be processed to obtain image segmentation result information; the image segmentation result information includes at least one object category information;

[0098] First data matching information is determined based on at least one object category information.

[0099] In some embodiments of this application, the information determination module 620 determines first data matching information based on at least one object category information, including:

[0100] Feature extraction is performed on the image information to be processed to obtain the first image feature information;

[0101] First data matching information is determined based on at least one object category information, first image feature information, and first association information; the first association information represents the correspondence between the object category information, image feature information, and data matching information.

[0102] In some embodiments of this application, the first association information is obtained by the information determination module 620 through the following steps:

[0103] Obtain the first image information;

[0104] Based on the first image information, determine the data matching information corresponding to each object category information;

[0105] Based on the data matching information corresponding to each object category and the second image feature information corresponding to the first image information, the first association information is determined.

[0106] In some embodiments of this application, each object category information includes first object category information. The information determination module 620 determines the data matching information corresponding to each object category information based on the first image information, including:

[0107] The first image information is input into the image processing model, and the first color information of the first image information is output through the image processing model.

[0108] Based on the original color information, the first color information, and the first region location information, the data matching information corresponding to the first object category information is determined; the original color information is the color data corresponding to the first image information input to the image processing model, and the first region location information represents the region location of the object corresponding to the first object category information in the first image information.

[0109] In some embodiments of this application, each object category information includes at least one second object category information. The information determination module 620 determines the data matching information corresponding to each object category information based on the first image information, including:

[0110] The first image information is subjected to color correction processing to obtain the second image information;

[0111] The second image information is color-converted based on the target color information corresponding to each second object category information to obtain the second color information corresponding to each second object category information.

[0112] Based on the original color information, second color information, and second region location information of the first image information, the data matching information corresponding to each second object category information is determined; the second region location information represents the region location of the object corresponding to each second object category information in the first image information.

[0113] In some embodiments of this application, the information determination module 620 performs feature extraction on the image information to be processed to obtain first image feature information, including:

[0114] Image analysis and processing are performed on the image information to be processed to obtain the illumination data of the image information to be processed;

[0115] Feature extraction is performed on the illumination data to obtain the first image feature information.

[0116] In some embodiments of this application, the first data matching information includes data matching information corresponding to at least two object category information. The image processing module 630 processes the image information to be processed based on the first data matching information to obtain the first target image information, including:

[0117] Based on the data matching information corresponding to each object category information, the image information to be processed is mapped to obtain the third image information corresponding to each object category information;

[0118] Based on the third image information corresponding to each object category information and the third region location information corresponding to each object category information, the first target image information is determined; the third region location information represents the region location of the object corresponding to each object category information in the image information to be processed.

[0119] In some embodiments of this application, the image processing module 630 determines the first target image information based on the third image information corresponding to each object category information and the third region location information corresponding to each object category information, including:

[0120] Binarization is performed on the image information to be processed based on the third region location information corresponding to each object category information to obtain the fourth image information corresponding to each object category information.

[0121] The third and fourth image information are fused to obtain the fifth image information corresponding to each object category information;

[0122] The fifth image information is fused to obtain the first target image information.

[0123] In some embodiments of this application, after the image processing module 630 processes the image information to be processed based on the first data matching information to obtain the first target image information, the image processing module 630 is further configured to:

[0124] The first target image information is used to locate the region to obtain at least one region information to be processed; each region information to be processed corresponds to at least two data matching information.

[0125] The data matching information corresponding to each region to be processed is fused to obtain the second data matching information corresponding to each region to be processed.

[0126] The first target image information is mapped based on the second data matching information and the information of the region to be processed to obtain the second target image information.

[0127] This application embodiment also provides a computer device, the computer device including:

[0128] One or more processors;

[0129] Memory; and

[0130] One or more applications, wherein the applications are stored in memory and configured to be executed by a processor from the steps of the image processing method in any of the above-described image processing method embodiments.

[0131] This application also provides a computer device, such as... Figure 7 As shown, it illustrates a structural schematic diagram of the computer device involved in the embodiments of this application, specifically:

[0132] The computer device may include components such as a processor 801 with one or more processing cores, a memory 802 with one or more computer-readable storage media, a power supply 803, and an input unit 804. Those skilled in the art will understand that... Figure 7 The computer device structure shown does not constitute a limitation on the computer device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0133] The processor 801 is the control center of the computer device. It connects various parts of the computer device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 802, and by calling data stored in the memory 802, it performs various functions of the computer device and processes data, thereby providing overall monitoring of the computer device. Optionally, the processor 801 may include one or more processing cores; preferably, the processor 801 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 801.

[0134] The memory 802 can be used to store software programs and modules. The processor 801 executes various functional applications and data processing by running the software programs and modules stored in the memory 802. The memory 802 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 802 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 802 may also include a memory controller to provide the processor 801 with access to the memory 802.

[0135] The computer device also includes a power supply 803 that supplies power to the various components. Preferably, the power supply 803 can be logically connected to the processor 801 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 803 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0136] The computer device may also include an input unit 804, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0137] Although not shown, the computer device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 801 in the computer device loads the executable files corresponding to the processes of one or more application programs into the memory 802 according to the following instructions, and the processor 801 runs the application programs stored in the memory 802 to realize various functions, as follows:

[0138] Obtain the image information to be processed;

[0139] Based on the image information to be processed, determine the first data matching information;

[0140] The image information to be processed is processed based on the first data matching information to obtain the first target image information.

[0141] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0142] Therefore, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in any of the image processing methods provided in embodiments of this application. For example, the computer program loaded by the processor can execute the following steps:

[0143] Obtain the image information to be processed;

[0144] Based on the image information to be processed, determine the first data matching information;

[0145] The image information to be processed is processed based on the first data matching information to obtain the first target image information.

[0146] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the detailed descriptions of other embodiments above, which will not be repeated here.

[0147] In practice, each of the above units or structures can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units or structures, please refer to the previous method embodiments, which will not be repeated here.

[0148] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0149] The above provides a detailed description of an image processing method and system for adjusting display quality according to the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method, characterized in that, include: Obtain the image information to be processed; Based on the image information to be processed, first data matching information is determined; The image information to be processed is processed based on the first data matching information to obtain the first target image information.

2. The method according to claim 1, characterized in that, The step of determining the first data matching information based on the image information to be processed includes: The image information to be processed is subjected to image segmentation processing to obtain image segmentation result information; the image segmentation result information includes at least one object category information; Based on the at least one object category information, first data matching information is determined.

3. The method according to claim 2, characterized in that, Determining the first data matching information based on the at least one object category information includes: Feature extraction is performed on the image information to be processed to obtain the first image feature information; Based on the at least one object category information, the first image feature information, and the first association information, first data matching information is determined; the first association information represents the correspondence between the object category information, the image feature information, and the data matching information.

4. The method according to claim 3, characterized in that, The first association information is obtained through the following steps: Obtain the first image information; Based on the first image information, determine the data matching information corresponding to each object category information; Based on the data matching information corresponding to each object category information and the second image feature information corresponding to the first image information, the first association information is determined.

5. The method according to claim 4, characterized in that, The object category information includes the first object category information; The step of determining the data matching information corresponding to each object category information based on the first image information includes: The first image information is input into the image processing model, and the first color information of the first image information is output through the image processing model. Based on the original color information, the first color information, and the first region location information, the data matching information corresponding to the first object category information is determined; the original color information is the color data corresponding to the first image information input to the image processing model, and the first region location information represents the region location of the object corresponding to the first object category information in the first image information.

6. The method according to claim 4, characterized in that, The object category information includes at least one second object category information; The step of determining the data matching information corresponding to each object category information based on the first image information includes: The first image information is subjected to color correction processing to obtain the second image information; The second image information is color-converted based on the target color information corresponding to each of the second object category information to obtain the second color information corresponding to each of the second object category information. Based on the original color information, the second color information, and the second region location information of the first image information, data matching information corresponding to each of the second object category information is determined; the second region location information represents the region location of the object corresponding to each of the second object category information in the first image information.

7. The method according to claim 3, characterized in that, The step of extracting features from the image information to be processed to obtain first image feature information includes: The image information to be processed is subjected to image analysis processing to obtain the illumination data of the image information to be processed; Feature extraction is performed on the illumination data to obtain the first image feature information.

8. The method according to claim 1, characterized in that, The first data matching information includes data matching information corresponding to at least two object category information; The step of processing the image information to be processed based on the first data matching information to obtain the first target image information includes: Based on the data matching information corresponding to each object category information, the image information to be processed is mapped to obtain the third image information corresponding to each object category information; Based on the third image information corresponding to each object category information and the third region location information corresponding to each object category information, the first target image information is determined; the third region location information represents the region location of the object corresponding to each object category information in the image information to be processed.

9. The method according to claim 8, characterized in that, The step of determining the first target image information based on the third image information corresponding to each object category information and the third region location information corresponding to each object category information includes: The image information to be processed is binarized based on the third region location information corresponding to each object category information to obtain the fourth image information corresponding to each object category information. The third image information and the fourth image information are fused to obtain the fifth image information corresponding to each object category information; The fifth image information is fused to obtain the first target image information.

10. The method according to any one of claims 1 to 9, characterized in that, After processing the image information to be processed based on the first data matching information to obtain the first target image information, the process includes: The first target image information is used to locate a region to obtain at least one region to be processed; each region to be processed corresponds to at least two data matching information. The data matching information corresponding to each of the regions to be processed is fused to obtain the second data matching information corresponding to each of the regions to be processed. Based on the second data matching information and the information of the region to be processed, the first target image information is mapped to obtain the second target image information.

11. A system, characterized in that, include: The information acquisition module is used to acquire information about the image to be processed. The information determination module is used to determine first data matching information based on the image information to be processed; The image processing module is used to process the image information to be processed based on the first data matching information to obtain the first target image information; Furthermore, the information determination module determines first data matching information based on the image information to be processed, including: The image information to be processed is subjected to image segmentation processing to obtain image segmentation result information; the image segmentation result information includes at least one object category information; Based on the at least one object category information, determine the first data matching information; Further, the information determination module determines first data matching information based on the at least one object category information, including: Feature extraction is performed on the image information to be processed to obtain the first image feature information; Based on the at least one object category information, the first image feature information, and the first association information, first data matching information is determined; the first association information represents the correspondence between the object category information, the image feature information, and the data matching information. Furthermore, the first association information is obtained by the information determination module through the following steps: Obtain the first image information; Based on the first image information, determine the data matching information corresponding to each object category information; Based on the data matching information corresponding to each object category information and the second image feature information corresponding to the first image information, the first association information is determined; Furthermore, the object category information includes first object category information, and the information determination module determines the data matching information corresponding to each object category information based on the first image information, including: The first image information is input into the image processing model, and the first color information of the first image information is output through the image processing model. Based on the original color information, the first color information, and the first region location information, the data matching information corresponding to the first object category information is determined; the original color information is the color data corresponding to the first image information input to the image processing model, and the first region location information represents the region location of the object corresponding to the first object category information in the first image information. Furthermore, the object category information includes at least one second object category information, and the information determination module determines the data matching information corresponding to each object category information based on the first image information, including: The first image information is subjected to color correction processing to obtain the second image information; The second image information is color-converted based on the target color information corresponding to each of the second object category information to obtain the second color information corresponding to each of the second object category information. Based on the original color information of the first image information, the second color information, and the second region location information, the data matching information corresponding to each of the second object category information is determined; the second region location information represents the region location of the object corresponding to each of the second object category information in the first image information. Further, the information determination module performs feature extraction on the image information to be processed to obtain first image feature information, including: The image information to be processed is subjected to image analysis processing to obtain the illumination data of the image information to be processed; Feature extraction is performed on the illumination data to obtain first image feature information; Further, the first data matching information includes data matching information corresponding to at least two object category information. The image processing module processes the image information to be processed based on the first data matching information to obtain the first target image information, including: Based on the data matching information corresponding to each object category information, the image information to be processed is mapped to obtain the third image information corresponding to each object category information; Based on the third image information corresponding to each object category information and the third region location information corresponding to each object category information, the first target image information is determined; the third region location information represents the region location of the object corresponding to each object category information in the image information to be processed. Further, the image processing module determines the first target image information based on the third image information corresponding to each object category information and the third region location information corresponding to each object category information, including: The image information to be processed is binarized based on the third region location information corresponding to each object category information to obtain the fourth image information corresponding to each object category information. The third image information and the fourth image information are fused to obtain the fifth image information corresponding to each object category information; The fifth image information is fused to obtain the first target image information; Furthermore, after the image processing module processes the image information to be processed based on the first data matching information to obtain the first target image information, the image processing module is further configured to: The first target image information is used to locate a region to obtain at least one region to be processed; each region to be processed corresponds to at least two data matching information. The data matching information corresponding to each of the regions to be processed is fused to obtain the second data matching information corresponding to each of the regions to be processed. Based on the second data matching information and the information of the region to be processed, the first target image information is mapped to obtain the second target image information.

12. A device, characterized in that, The device includes: One or more processors; Memory; and One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, It contains a computer program that is loaded by a processor to perform the steps of the method according to any one of claims 1 to 10.

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