Data processing method and system based on big data

Abstract

The embodiment of the application discloses a data processing method and system based on big data, which is applied to an electronic device, and the method comprises the following steps: acquiring a target ambient light brightness value; acquiring a to-be-processed image when the target ambient light brightness value is lower than a preset threshold; acquiring target attribute information of the to-be-processed image; determining a target image enhancement processing algorithm and a target image enhancement algorithm control parameter corresponding to the target attribute information, wherein the target image enhancement processing algorithm and the target image enhancement algorithm control parameter are obtained by using big data technology; the target image enhancement algorithm control parameter is used for controlling an image enhancement effect of the target image enhancement processing algorithm; and performing image enhancement processing on the to-be-processed image according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter to obtain a target image. By using the embodiment of the application, the image quality can be improved in a dark visual environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data, in particular to a data processing method and system based on big data. BACKGROUND

[0002] With the rapid development of science and technology, the photographing function is becoming more and more a standard technology of electronic devices (such as mobile phones, tablet computers, etc.). In the photographing scene, users have higher and higher requirements for image quality. The quality of the image greatly affects the product evaluation of the electronic device by the user, thereby relating to the sales of the electronic device. Especially in the dark visual environment, the shooting effect is often not ideal. Therefore, how to improve the image quality in the dark visual environment is an urgent problem to be solved. SUMMARY

[0003] The embodiments of the present application provide a data processing method and system based on big data, which can improve the image quality in the dark visual environment.

[0004] In a first aspect, the embodiments of the present application provide a data processing method based on big data, applied to an electronic device, the method comprising:

[0005] obtaining a target ambient light brightness value;

[0006] when the target ambient light brightness value is lower than a preset threshold, obtaining a to-be-processed image;

[0007] obtaining target attribute information of the to-be-processed image;

[0008] determining a target image enhancement processing algorithm and a target image enhancement algorithm control parameter corresponding to the target attribute information, the target image enhancement processing algorithm and the target image enhancement algorithm control parameter being obtained by using big data technology; the target image enhancement algorithm control parameter is used to control the image enhancement effect of the target image enhancement processing algorithm;

[0009] performing image enhancement processing on the to-be-processed image according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter to obtain a target image.

[0010] In a second aspect, the embodiments of the present application provide a data processing system based on big data, applied to an electronic device, the system comprising: an obtaining unit, a determining unit and an image enhancement processing unit, wherein,

[0011] the obtaining unit is configured to obtain a target ambient light brightness value; when the target ambient light brightness value is lower than a preset threshold, obtain a to-be-processed image; and obtain target attribute information of the to-be-processed image;

[0012] The determining unit is configured to determine a target image enhancement processing algorithm and a target image enhancement algorithm control parameter corresponding to the target attribute information, wherein the target image enhancement processing algorithm and the target image enhancement algorithm control parameter are obtained by using big data technology; and the target image enhancement algorithm control parameter is used to control an image enhancement effect of the target image enhancement processing algorithm.

[0013] The image enhancement processing unit is configured to perform image enhancement processing on the to-be-processed image according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter, to obtain a target image.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, a human body communication chip and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the present application.

[0015] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to perform some or all of the steps described in the first aspect of the present application.

[0016] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the present application. The computer program product can be a software installation package.

[0017] By implementing the embodiments of the present application, the following beneficial effects are achieved:

[0018] It can be seen that the data processing method and system based on big data described in the embodiments of the present application are applied to an electronic device, a target ambient light brightness value is obtained, when the target ambient light brightness value is lower than a preset threshold value, a to-be-processed image is obtained, target attribute information of the to-be-processed image is obtained, a target image enhancement processing algorithm corresponding to the target attribute information and a target image enhancement algorithm control parameter are determined, the target image enhancement processing algorithm and the target image enhancement algorithm control parameter are obtained by using big data technology, the target image enhancement algorithm control parameter is used to control the image enhancement effect of the target image enhancement processing algorithm, the to-be-processed image is subjected to image enhancement processing according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter, and a target image is obtained. In the dark visual environment, the image enhancement processing algorithm corresponding to the attribute of the to-be-processed image and the corresponding algorithm control parameter can be selected by using big data technology, so as to ensure the image enhancement effect and help improve the image quality in the dark visual environment. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 is a flow diagram of a data processing method based on big data provided by the embodiments of the present application;

[0021] Figure 2 is a structural schematic diagram of an electronic device provided by the embodiments of the present application;

[0022] Figure 3 is a functional unit composition block diagram of a data processing system based on big data provided by the embodiments of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] The terms "first", "second", and the like in the description and in the claims of the present application and above drawings are used for distinguishing between similar objects, not for describing a particular sequential order. The terms "comprises", "comprising", "includes", "including" and the like are to be construed open- ended, meaning that they include the listed steps or elements, but not excluding other steps or elements. For example, a process, method, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements, but can include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

[0025] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the application. The appearances of the phrase "in an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. It is expressly understood that any of the embodiments described herein can be combined with any of the other embodiments unless specifically noted otherwise.

[0026] In the embodiments of the present application, the electronic device can include at least one of a mobile phone, a tablet computer, a wearable device, a server, and the like, and the like, and the like, and the server can include a cloud server.

[0027] The embodiments of the present application will be described in detail below.

[0028] Please refer to Figure 1 , Figure 1 is a flowchart of a data processing method based on big data provided by the embodiments of the present application, applied to an electronic device, and the data processing method based on big data can include the following steps:

[0029] 101, obtaining a target ambient light brightness value.

[0030] In the embodiments of the present application, the target ambient light brightness value can be obtained by using an ambient light sensor during the shooting process.

[0031] 102, obtaining a to-be-processed image when the target ambient light brightness value is lower than a preset threshold.

[0032] The preset threshold can be set in advance or by default by the system. The to-be-processed image can be any shooting image.

[0033] In the embodiments of the present application, when the target ambient light brightness value is lower than the preset threshold, it means that it is in a dark environment, and then the to-be-processed image can be obtained, and the to-be-processed image can be processed by image enhancement to improve the image quality.

[0034] 103, obtaining target attribute information of the to-be-processed image.

[0035] In the embodiments of the present application, the target attribute information can include at least one of the following: a shooting scene, an area ratio between a background image and a foreground image in the to-be-processed image, an image quality ratio between the background image and the foreground image in the to-be-processed image, and the like, which are not limited herein.

[0036] In a specific implementation, the to-be-processed image can be analyzed, such as image segmentation or image quality evaluation, to obtain the target attribute information of the to-be-processed image.

[0037] 104. determining a target image enhancement processing algorithm and a target image enhancement algorithm control parameter corresponding to the target attribute information, wherein the target image enhancement processing algorithm and the target image enhancement algorithm control parameter are obtained by using big data technology; and the target image enhancement algorithm control parameter is used to control the image enhancement effect of the target image enhancement processing algorithm.

[0038] In the embodiments of the present application, a mapping relationship between the preset attribute information and the image enhancement processing algorithm can be preset, and then the target image enhancement processing algorithm corresponding to the target attribute information can be determined based on the mapping relationship. Of course, the mapping relationship can be obtained by using big data technology.

[0039] Of course, a mapping relationship between the preset attribute information and the image enhancement processing algorithm control parameter can also be set, and then the target image enhancement processing algorithm control parameter corresponding to the target attribute information can be determined based on the mapping relationship. Of course, the mapping relationship can be obtained by using big data technology.

[0040] The target image enhancement algorithm control parameter is used to control the image enhancement effect of the target image enhancement processing algorithm, and thus the image enhancement effect can be accurately controlled.

[0041] Optionally, the target attribute information includes an area ratio between a background image and a foreground image in the to-be-processed image and an image quality ratio between the background image and the foreground image in the to-be-processed image.

[0042] The step 104 of determining the target image enhancement processing algorithm and the target image enhancement algorithm control parameter corresponding to the target attribute information can include the following steps:

[0043] 41. obtaining M reference images corresponding to the target ambient light brightness value from a preset image library, each reference image corresponding to an enhanced image after image enhancement processing; M is an integer greater than 1;

[0044] 42. screening the M reference images according to the area ratio to obtain N reference images; N is an integer greater than 1 and less than or equal to M;

[0045] 43. obtaining an enhanced image corresponding to each of the N reference images to obtain N enhanced images;

[0046] 44. obtaining an image quality evaluation value of each of the N enhanced images to obtain N image quality evaluation values;

[0047] 45. selecting an image quality evaluation value greater than a preset image quality evaluation value from the N image quality evaluation values to obtain K image quality evaluation values; K is an integer greater than 1 and less than or equal to N;

[0048] 46. obtaining a reference image corresponding to each of the K image quality evaluation values to obtain K reference images;

[0049] 47. classifying the K reference images according to the algorithm type of the image enhancement processing algorithm to obtain P reference image sets, each reference image set including at least one reference image, and P being a positive integer less than K;

[0050] 48. determining the target image enhancement processing algorithm and the target image enhancement algorithm control parameter according to the P reference image sets.

[0051] In the embodiments of the present application, the preset image library can be a cloud image library, the preset image library including a large number of stored images, each image corresponding to an ambient brightness value at the time of shooting, and each image also corresponding to an enhanced image after image enhancement processing, i.e., each image corresponding to a corresponding image enhancement processing algorithm and image enhancement algorithm control parameter.

[0052] In a specific implementation, M reference images corresponding to the target ambient brightness value can be obtained from the preset image library, each reference image corresponding to an enhanced image after image enhancement processing; M is an integer greater than 1, i.e., the difference between the ambient brightness value of each image in the M reference images and the target ambient brightness value is within a first preset range, which can be pre-set or system default, so as to ensure that the shooting environments of the selected images are similar dark vision environments.

[0053] Next, the M reference images can also be selected according to the area ratio to obtain N reference images; N is an integer greater than 1 and less than or equal to M, i.e., the difference between the area ratio of each image in the N reference images and the area ratio between the background image and the foreground image in the to-be-processed image is within a second preset range, which can be pre-set or system default, so as to ensure that the shooting environments of the selected images are further similar.

[0054] Further, the preset image quality evaluation value can also be preset or system default, the enhanced image corresponding to each reference image in the N reference images can be acquired, N enhanced images are obtained, the image quality evaluation values of the N enhanced images are acquired, N image quality evaluation values are obtained, the image quality evaluation values greater than the preset image quality evaluation value are selected from the N image quality evaluation values, K image quality evaluation values are obtained; K is an integer greater than 1 and less than or equal to N, and then, in the case that the shooting environment is similar, the image with good image enhancement effect can be screened.

[0055] Next, the reference images corresponding to the K image quality evaluation values are acquired, K reference images are obtained, the K reference images are classified according to the algorithm type of the image enhancement processing algorithm, P reference image sets are obtained, each reference image set includes at least one reference image, P is a positive integer less than K, the target image enhancement processing algorithm and the target image enhancement algorithm control parameter are determined according to the P reference image sets, in this way, under the condition that the shooting environment is similar, the images with the same kind of image enhancement processing algorithm with good image enhancement effect are screened out, the final image enhancement processing algorithm and the corresponding image enhancement processing algorithm control parameter are determined, which is helpful to ensure the image enhancement effect and improve the image quality.

[0056] Further, the above step 48 of determining the target image enhancement processing algorithm and the target image enhancement algorithm control parameter according to the P reference image sets can include the following steps:

[0057] 481, the image quality evaluation mean of the enhanced image of each reference image set in the P reference image sets is determined, P image quality evaluation means are obtained;

[0058] 482, the P image quality evaluation means and the number of images in each reference image set in the P reference image sets are weighted, P image enhancement processing algorithm evaluation values are obtained;

[0059] 483, the maximum value in the P image enhancement processing algorithm evaluation values is selected, and the image enhancement processing algorithm corresponding to the maximum value is taken as the target image enhancement processing algorithm;

[0060] 484, the target image enhancement algorithm control parameter corresponding to the target image enhancement processing algorithm is determined.

[0061] In the embodiments of the present application, the image quality evaluation mean of the enhanced image of each reference image set in the P reference image sets can be determined to obtain P image quality evaluation means, that is, the image quality evaluation value of the enhanced image of each reference image set is determined, and then the image quality evaluation value of the enhanced image of each reference image set is subjected to mean operation to obtain P image quality evaluation means, and then the P image quality evaluation means and the number of images of each reference image set in the P reference image sets are subjected to weighted operation to obtain P image enhancement processing algorithm evaluation values. The image quality evaluation mean reflects the pros and cons of the enhancement effect of an image enhancement processing algorithm, and the number of images of each reference image set reflects the preference of a user for the enhancement of each image enhancement processing algorithm. Therefore, an algorithm that is popular among the public and has a good image enhancement effect can be selected in combination with the two dimensions.

[0062] Next, the maximum value in the P image enhancement processing algorithm evaluation values is selected, and the image enhancement processing algorithm corresponding to the maximum value is taken as a target image enhancement processing algorithm, and the target image enhancement algorithm control parameter corresponding to the target image enhancement processing algorithm is determined. For example, the image enhancement algorithm control parameter corresponding to one or more reference images in the target image enhancement processing algorithm can be taken as the target image enhancement algorithm control parameter. In this way, an algorithm that is popular among the public and has a good image enhancement effect can be obtained by using big data.

[0063] Further, the step 482 of performing weighted operation according to the P image quality evaluation means and the number of images of each reference image set in the P reference image sets to obtain P image enhancement processing algorithm evaluation values can include the following steps:

[0064] 4821. Obtain the number of images j corresponding to the reference image set corresponding to the image quality evaluation mean i, wherein the image quality evaluation mean i is any image quality evaluation mean in the P image quality evaluation means;

[0065] 4822. Determine the target weight value corresponding to the number of images j;

[0066] 4823. Perform weighted operation according to the target weight value and the image quality evaluation mean i to obtain the corresponding image enhancement processing algorithm evaluation value.

[0067] In the embodiments of the present application, taking the image quality evaluation mean i as an example, the image quality evaluation mean i is any one of the P image quality evaluation means, the image quantity j corresponding to the reference image set corresponding to the image quality evaluation mean i is obtained, and then the target weight value corresponding to the image quantity j is determined according to the preset mapping relationship between the image quantity and the weight value. Then, the target weight value and the image quality evaluation mean i are weighted and operated to obtain the corresponding image enhancement processing algorithm evaluation value. In this way, the determined image enhancement processing algorithm not only has a good image enhancement effect, but also meets the public selection.

[0068] Optionally, the step 484 of determining the target image enhancement algorithm control parameter corresponding to the target image enhancement processing algorithm can include the following steps:

[0069] 4841. Obtain the reference image set corresponding to the maximum value;

[0070] 4842. Determine the images in the reference image set whose image quality evaluation values are greater than a set threshold value to obtain a target reference image set;

[0071] 4843. Obtain the image enhancement algorithm control parameter corresponding to each image in the target reference image set to obtain at least one image enhancement algorithm control parameter;

[0072] 4844. Determine the target image enhancement algorithm control parameter according to the at least one image enhancement algorithm control parameter.

[0073] In the embodiments of the present application, the set threshold value can be pre-set or system default.

[0074] In specific implementation, the reference image set corresponding to the maximum value can be obtained, the images in the reference image set whose image quality evaluation values are greater than a set threshold value are determined to obtain a target reference image set, the image enhancement algorithm control parameter corresponding to each image in the target reference image set is obtained to obtain at least one image enhancement algorithm control parameter, and the target image enhancement algorithm control parameter is determined according to the at least one image enhancement algorithm control parameter. In this way, the image enhancement algorithm control parameter of the image with good image quality is selected from the selected image enhancement algorithm to determine the final image enhancement algorithm control parameter, which ensures that the image enhancement effect is further improved and helps to improve the image quality.

[0075] Optionally, the step 4844 of determining the target image enhancement algorithm control parameter according to the at least one image enhancement algorithm control parameter can include the following steps:

[0076] S1. Determine the reference image enhancement algorithm control parameter in the at least one image enhancement algorithm control parameter;

[0077] S2, determine a target fluctuation parameter of the at least one image enhancement algorithm control parameter;

[0078] S3, adjust the reference image enhancement algorithm control parameter according to the target fluctuation parameter, to obtain the target image enhancement algorithm control parameter.

[0079] In the embodiments of the present application, a reference image enhancement algorithm control parameter in the at least one image enhancement algorithm control parameter can be determined, which can be one image enhancement algorithm control parameter in the at least one image enhancement algorithm control parameter, for example, the reference image enhancement algorithm control parameter can be the image enhancement algorithm control parameter of the image with the best image quality in the at least one image enhancement algorithm control parameter, then the at least one image enhancement algorithm control parameter can be subjected to mean square deviation operation to obtain a target mean square deviation, and the target mean square deviation is taken as the target fluctuation parameter.

[0080] Next, a target adjustment coefficient corresponding to the target fluctuation parameter can be determined according to a preset mapping relationship between the fluctuation parameter and the adjustment coefficient, and the reference image enhancement algorithm control parameter is adjusted according to the target adjustment coefficient to obtain the target image enhancement algorithm control parameter, so that the fluctuation of the image enhancement algorithm control parameter can be considered to further regulate the corresponding algorithm control parameter, which helps to ensure the image enhancement effect and improve the image quality.

[0081] In actual application, the image enhancement algorithm control parameter can also be multiple parameters, which can include adjustable parameters and non-adjustable parameters, and then the adjustable parameters can be adjusted based on the target adjustment coefficient.

[0082] 105, perform image enhancement processing on the to-be-processed image according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter, to obtain a target image.

[0083] In the embodiments of the present application, the to-be-processed image can be subjected to image enhancement processing according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter, to obtain a target image, so as to help to ensure the image enhancement effect and improve the image quality.

[0084] It can be seen that the data processing method based on big data described in the embodiment of the application is applied to an electronic device, a target ambient light brightness value is obtained, when the target ambient light brightness value is lower than a preset threshold value, a to-be-processed image is obtained, target attribute information of the to-be-processed image is obtained, a target image enhancement processing algorithm corresponding to the target attribute information and a target image enhancement algorithm control parameter are determined, the target image enhancement processing algorithm and the target image enhancement algorithm control parameter are obtained by using big data technology, the target image enhancement algorithm control parameter is used to control an image enhancement effect of the target image enhancement processing algorithm, the to-be-processed image is subjected to image enhancement processing according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter, and a target image is obtained. In the dark visual environment, the image enhancement processing algorithm corresponding to the attribute of the to-be-processed image and the corresponding algorithm control parameter can be selected by using big data technology, so that the image enhancement effect is ensured, and the image quality is improved in the dark visual environment.

[0085] Consistent with the above embodiment, please refer to Figure 2 , Figure 2 is a structural schematic diagram of an electronic device provided by the embodiment of the application, as shown in the figure, the electronic device comprises a processor, a memory, a communication interface and one or more programs, the above-mentioned one or more programs are stored in the above-mentioned memory, and are configured to be executed by the above-mentioned processor, in the embodiment of the application, the program comprises instructions for executing the following steps:

[0086] obtaining a target ambient light brightness value;

[0087] when the target ambient light brightness value is lower than a preset threshold value, obtaining a to-be-processed image;

[0088] obtaining target attribute information of the to-be-processed image;

[0089] determining a target image enhancement processing algorithm corresponding to the target attribute information and a target image enhancement algorithm control parameter, the target image enhancement processing algorithm and the target image enhancement algorithm control parameter are obtained by using big data technology; the target image enhancement algorithm control parameter is used to control the image enhancement effect of the target image enhancement processing algorithm;

[0090] according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter, the to-be-processed image is subjected to image enhancement processing, and a target image is obtained.

[0091] Optionally, the target attribute information comprises an area ratio between a background image and a foreground image in the to-be-processed image and an image quality ratio between the background image and the foreground image in the to-be-processed image;

[0092] In the determining of the target image enhancement processing algorithm and the target image enhancement algorithm control parameter corresponding to the target attribute information, the program includes instructions for performing the following steps:

[0093] M reference images corresponding to the target ambient light brightness value are obtained from a preset image library, each reference image corresponding to an enhanced image after image enhancement processing; M is an integer greater than 1;

[0094] The M reference images are screened according to the area ratio to obtain N reference images; N is an integer greater than 1 and less than or equal to M;

[0095] The enhanced image corresponding to each reference image in the N reference images is obtained to obtain N enhanced images;

[0096] Image quality evaluation values of the N enhanced images are obtained to obtain N image quality evaluation values;

[0097] From the N image quality evaluation values, image quality evaluation values greater than a preset image quality evaluation value are selected to obtain K image quality evaluation values; K is an integer greater than 1 and less than or equal to N;

[0098] Reference images corresponding to the K image quality evaluation values are obtained to obtain K reference images;

[0099] The K reference images are classified according to the algorithm type of the image enhancement processing algorithm to obtain P reference image sets, each reference image set including at least one reference image, and P being a positive integer less than K;

[0100] The target image enhancement processing algorithm and the target image enhancement algorithm control parameter are determined according to the P reference image sets.

[0101] Optionally, in the determining of the target image enhancement processing algorithm and the target image enhancement algorithm control parameter according to the P reference image sets, the program includes instructions for performing the following steps:

[0102] Image quality evaluation means of the enhanced images of each reference image set in the P reference image sets are determined to obtain P image quality evaluation means;

[0103] The P image quality evaluation means and the number of images of each reference image set in the P reference image sets are weighted to obtain P image enhancement processing algorithm evaluation values;

[0104] The maximum value in the P image enhancement processing algorithm evaluation values is selected, and the image enhancement processing algorithm corresponding to the maximum value is taken as the target image enhancement processing algorithm;

[0105] determining the target image enhancement algorithm control parameter corresponding to the target image enhancement algorithm.

[0106] Optionally, in the aspect of performing weighted operation according to the P image quality evaluation averages and the image quantity of each reference image set, the program comprises instructions for performing the following steps:

[0107] acquiring the image quantity j corresponding to the reference image set corresponding to the image quality evaluation average i, the image quality evaluation average i being any image quality evaluation average in the P image quality evaluation averages;

[0108] determining the target weight value corresponding to the image quantity j;

[0109] performing weighted operation according to the target weight value and the image quality evaluation average i to obtain the corresponding image enhancement processing algorithm evaluation value.

[0110] Optionally, in the aspect of determining the target image enhancement algorithm control parameter corresponding to the target image enhancement algorithm, the program comprises instructions for performing the following steps:

[0111] acquiring the reference image set corresponding to the maximum value;

[0112] determining the image with the image quality evaluation value greater than the set threshold in the reference image set to obtain a target reference image set;

[0113] acquiring the image enhancement algorithm control parameter corresponding to each image in the target reference image set to obtain at least one image enhancement algorithm control parameter;

[0114] determining the target image enhancement algorithm control parameter according to the at least one image enhancement algorithm control parameter.

[0115] Optionally, in the aspect of determining the target image enhancement algorithm control parameter according to the at least one image enhancement algorithm control parameter, the program comprises instructions for performing the following steps:

[0116] determining the reference image enhancement algorithm control parameter in the at least one image enhancement algorithm control parameter;

[0117] determining the target fluctuation parameter of the at least one image enhancement algorithm control parameter;

[0118] adjusting the reference image enhancement algorithm control parameter according to the target fluctuation parameter to obtain the target image enhancement algorithm control parameter.

[0119] It can be seen that the electronic device described in the embodiment of the application obtains a target ambient light brightness value, obtains a to-be-processed image when the target ambient light brightness value is lower than a preset threshold, obtains target attribute information of the to-be-processed image, determines a target image enhancement processing algorithm and a target image enhancement algorithm control parameter corresponding to the target attribute information, the target image enhancement processing algorithm and the target image enhancement algorithm control parameter are obtained by using big data technology, the target image enhancement algorithm control parameter is used to control the image enhancement effect of the target image enhancement processing algorithm, the to-be-processed image is subjected to image enhancement processing according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter, and a target image is obtained. In the dark visual environment, the image enhancement processing algorithm corresponding to the attribute of the to-be-processed image and the corresponding algorithm control parameter can be selected by using big data technology, so as to ensure the image enhancement effect and help improve the image quality in the dark visual environment.

[0120] Figure 3 is a functional unit composition block diagram of a data processing system 300 based on big data involved in the embodiment of the application, applied to an electronic device, the system 300 comprises: an acquisition unit 301, a determination unit 302 and an image enhancement processing unit 303, wherein,

[0121] The acquisition unit 301 is configured to obtain a target ambient light brightness value, obtain a to-be-processed image when the target ambient light brightness value is lower than a preset threshold, and obtain target attribute information of the to-be-processed image.

[0122] The determination unit 302 is configured to determine a target image enhancement processing algorithm and a target image enhancement algorithm control parameter corresponding to the target attribute information, the target image enhancement processing algorithm and the target image enhancement algorithm control parameter are obtained by using big data technology, and the target image enhancement algorithm control parameter is used to control the image enhancement effect of the target image enhancement processing algorithm.

[0123] The image enhancement processing unit 303 is configured to perform image enhancement processing on the to-be-processed image according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter, and obtain a target image.

[0124] Optionally, the target attribute information comprises an area ratio between a background image and a foreground image in the to-be-processed image and an image quality ratio between the background image and the foreground image in the to-be-processed image.

[0125] In the aspect of determining the target image enhancement processing algorithm and the target image enhancement algorithm control parameter corresponding to the target attribute information, the determination unit 302 is specifically configured to:

[0126] acquire M reference images corresponding to the target ambient light brightness value from a preset image library, each reference image corresponding to an enhanced image after image enhancement processing; M is an integer greater than 1;

[0127] screen the M reference images according to the area ratio to obtain N reference images; N is an integer greater than 1 and less than or equal to M;

[0128] acquire the enhanced image corresponding to each reference image in the N reference images to obtain N enhanced images;

[0129] acquire the image quality evaluation value of the N enhanced images to obtain N image quality evaluation values;

[0130] select the image quality evaluation value greater than a preset image quality evaluation value from the N image quality evaluation values to obtain K image quality evaluation values; K is an integer greater than 1 and less than or equal to N;

[0131] acquire the reference image corresponding to the K image quality evaluation values to obtain K reference images;

[0132] classify the K reference images according to the algorithm type of the image enhancement processing algorithm to obtain P reference image sets, each reference image set including at least one reference image, and P is a positive integer less than K;

[0133] determine the target image enhancement processing algorithm and the target image enhancement algorithm control parameter according to the P reference image sets.

[0134] Optionally, in the aspect of determining the target image enhancement processing algorithm and the target image enhancement algorithm control parameter according to the P reference image sets, the determination unit 302 is specifically configured to:

[0135] determine the image quality evaluation mean value of the enhanced image of each reference image set in the P reference image sets to obtain P image quality evaluation mean values;

[0136] perform weighted operation according to the P image quality evaluation mean values and the number of images of each reference image set in the P reference image sets to obtain P image enhancement processing algorithm evaluation values;

[0137] select the maximum value in the P image enhancement processing algorithm evaluation values, and take the image enhancement processing algorithm corresponding to the maximum value as the target image enhancement processing algorithm;

[0138] determine the target image enhancement algorithm control parameter corresponding to the target image enhancement processing algorithm.

[0139] Optionally, in the aspect of performing weighted operation according to the P image quality evaluation averages and the image quantity of each reference image set in the P reference image sets to obtain P image enhancement processing algorithm evaluation values, the determining unit 302 is specifically configured to:

[0140] obtain an image quantity j corresponding to a reference image set corresponding to an image quality evaluation average i, the image quality evaluation average i being any image quality evaluation average in the P image quality evaluation averages;

[0141] determine a target weight value corresponding to the image quantity j;

[0142] perform weighted operation according to the target weight value and the image quality evaluation average i to obtain a corresponding image enhancement processing algorithm evaluation value.

[0143] Optionally, in the aspect of determining the target image enhancement algorithm control parameter corresponding to the target image enhancement processing algorithm, the determining unit 302 is specifically configured to:

[0144] obtain a reference image set corresponding to the maximum value;

[0145] determine an image with an image quality evaluation value greater than a set threshold value in the reference image set to obtain a target reference image set;

[0146] obtain an image enhancement algorithm control parameter corresponding to each image in the target reference image set to obtain at least one image enhancement algorithm control parameter;

[0147] determine the target image enhancement algorithm control parameter according to the at least one image enhancement algorithm control parameter.

[0148] Optionally, in the aspect of determining the target image enhancement algorithm control parameter according to the at least one image enhancement algorithm control parameter, the determining unit 302 is specifically configured to:

[0149] determine a reference image enhancement algorithm control parameter in the at least one image enhancement algorithm control parameter;

[0150] determine a target fluctuation parameter of the at least one image enhancement algorithm control parameter;

[0151] adjust the reference image enhancement algorithm control parameter according to the target fluctuation parameter to obtain the target image enhancement algorithm control parameter.

[0152] It can be seen that the data processing system based on big data described in the embodiment of the application is applied to an electronic device, target ambient light brightness value is acquired, when the target ambient light brightness value is lower than a preset threshold, a to-be-processed image is acquired, target attribute information of the to-be-processed image is acquired, a target image enhancement processing algorithm corresponding to the target attribute information and a target image enhancement algorithm control parameter are determined, the target image enhancement processing algorithm and the target image enhancement algorithm control parameter are obtained by using big data technology, the target image enhancement algorithm control parameter is used to control an image enhancement effect of the target image enhancement processing algorithm, the to-be-processed image is subjected to image enhancement processing according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter, and a target image is obtained. In the dark visual environment, the image enhancement processing algorithm corresponding to the attribute of the to-be-processed image and the corresponding algorithm control parameter can be selected by using big data technology, so that the image enhancement effect is ensured, and the image quality is improved in the dark visual environment.

[0153] It can be understood that the functions of the program modules of the data processing system based on big data in the embodiment can be specifically implemented according to the methods in the above method embodiments, and the specific implementation process can refer to the related description of the above method embodiments, which will not be described here.

[0154] The embodiment of the application further provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to execute part or all steps of any method described in the above method embodiments.

[0155] The embodiment of the application further provides a computer program product, and the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all steps of any method described in the above method embodiments. The computer program product can be a software installation package.

[0156] It should be noted that, for each of the above method embodiments, in order to simply describe, each is described as a combination of a series of actions, but those skilled in the art should know that the application is not limited to the order of the actions described, because according to the application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the application.

[0157] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0158] In several embodiments provided in the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the apparatus embodiments described above is merely illustrative, and the division of the units can be changed according to actual needs. For example, the units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0159] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0160] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0161] When the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0162] Those of ordinary skill in the art can understand that all or part of the steps of the various methods in the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer readable storage medium, which can include a flash disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, etc.

[0163] The above has introduced the embodiments of the present application in detail, and the principles and implementation manners of the present application are described by applying specific examples. The above embodiment description is only used for helping understanding the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, according to the idea of the present application, the specific implementation manner and application range will have changes, and the above is not understood as the limitation of the present application.

Claims

1. A big data-based data processing method, characterized by, The method is applied to an electronic device and comprises the following steps: obtaining a target ambient light brightness value; when the target ambient light brightness value is lower than a preset threshold, obtaining a to-be-processed image; obtaining target attribute information of the to-be-processed image; determining a target image enhancement processing algorithm and a target image enhancement algorithm control parameter corresponding to the target attribute information, wherein the target image enhancement processing algorithm and the target image enhancement algorithm control parameter are obtained by using big data technology, and the target image enhancement algorithm control parameter is used to control the image enhancement effect of the target image enhancement processing algorithm; performing image enhancement processing on the to-be-processed image according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter to obtain a target image; wherein the target attribute information comprises an area ratio between a background image and a foreground image in the to-be-processed image and an image quality ratio between the background image and the foreground image in the to-be-processed image; the determination of the target image enhancement processing algorithm and the target image enhancement algorithm control parameter corresponding to the target attribute information comprises the following steps: obtaining M reference images corresponding to the target ambient light brightness value from a preset image library, wherein each reference image corresponds to an enhanced image after image enhancement processing, M is an integer greater than 1; screening the M reference images according to the area ratio to obtain N reference images, wherein N is an integer greater than 1 and less than or equal to M; obtaining an enhanced image corresponding to each reference image in the N reference images to obtain N enhanced images; obtaining image quality evaluation values of the N enhanced images to obtain N image quality evaluation values; selecting image quality evaluation values greater than a preset image quality evaluation value from the N image quality evaluation values to obtain K image quality evaluation values, wherein K is an integer greater than 1 and less than or equal to N; obtaining reference images corresponding to the K image quality evaluation values to obtain K reference images; classifying the K reference images according to the algorithm types of image enhancement processing algorithms to obtain P reference image sets, wherein each reference image set comprises at least one reference image, and P is a positive integer less than K; determining the target image enhancement processing algorithm and the target image enhancement algorithm control parameter according to the P reference image sets.

2. The method of claim 1, wherein, the determination of the target image enhancement processing algorithm and the target image enhancement algorithm control parameter according to the P reference image sets comprises the following steps: determining an image quality evaluation mean value of the enhanced images in each reference image set in the P reference image sets to obtain P image quality evaluation mean values; performing weighted operation according to the P image quality evaluation mean values and the number of images in each reference image set in the P reference image sets to obtain P image enhancement processing algorithm evaluation values; selecting a maximum value in the P image enhancement processing algorithm evaluation values, and taking an image enhancement processing algorithm corresponding to the maximum value as the target image enhancement processing algorithm; determining the target image enhancement algorithm control parameter corresponding to the target image enhancement processing algorithm.

3. The method of claim 2, wherein, The P image quality evaluation mean values and the number of images in each reference image set are subjected to weighted operation to obtain P image enhancement processing algorithm evaluation values, including: An image quantity j corresponding to a reference image set corresponding to an image quality evaluation mean value i is acquired, the image quality evaluation mean value i being any image quality evaluation mean value in the P image quality evaluation mean values; A target weight value corresponding to the image quantity j is determined; The target weight value and the image quality evaluation mean value i are subjected to weighted operation to obtain a corresponding image enhancement processing algorithm evaluation value.

4. The method of claim 2, wherein, The target image enhancement algorithm control parameter corresponding to the target image enhancement processing algorithm is determined, including: A reference image set corresponding to the maximum value is acquired; Images in the reference image set with image quality evaluation values greater than a set threshold value are determined to obtain a target reference image set; Image enhancement algorithm control parameters corresponding to each image in the target reference image set are acquired to obtain at least one image enhancement algorithm control parameter; The target image enhancement algorithm control parameter is determined according to the at least one image enhancement algorithm control parameter.

5. The method of claim 4, wherein, The target image enhancement algorithm control parameter is determined according to the at least one image enhancement algorithm control parameter, including: A reference image enhancement algorithm control parameter in the at least one image enhancement algorithm control parameter is determined; A target fluctuation parameter of the at least one image enhancement algorithm control parameter is determined; The target image enhancement algorithm control parameter is obtained by adjusting the reference image enhancement algorithm control parameter according to the target fluctuation parameter.

6. A big data based data processing system, characterized by, The system is applied to an electronic device and includes an acquisition unit, a determination unit and an image enhancement processing unit, wherein: The acquisition unit is configured to acquire a target ambient light brightness value, acquire a to-be-processed image when the target ambient light brightness value is lower than a preset threshold, and acquire target attribute information of the to-be-processed image; The determination unit is configured to determine a target image enhancement processing algorithm and a target image enhancement algorithm control parameter corresponding to the target attribute information, the target image enhancement processing algorithm and the target image enhancement algorithm control parameter being obtained by using big data technology, and the target image enhancement algorithm control parameter being used to control an image enhancement effect of the target image enhancement processing algorithm; The image enhancement processing unit is configured to perform image enhancement processing on the to-be-processed image according to the target image enhancement processing algorithm and the target image enhancement algorithm control parameter to obtain a target image. The target attribute information includes an area ratio between a background image and a foreground image in the to-be-processed image and an image quality ratio between the background image and the foreground image in the to-be-processed image. In the aspect of determining the target image enhancement processing algorithm and the target image enhancement algorithm control parameter corresponding to the target attribute information, the determination unit is specifically configured to: M reference images corresponding to the target ambient light brightness value are acquired from a preset image library, each reference image corresponding to an enhanced image after image enhancement processing; M is an integer greater than 1. Screen the M reference images according to the area ratio, to obtain N reference images; N is an integer greater than 1 and less than or equal to M; Obtain an enhanced image corresponding to each of the N reference images, to obtain N enhanced images; Obtain an image quality evaluation value of each of the N enhanced images, to obtain N image quality evaluation values; Select an image quality evaluation value greater than a preset image quality evaluation value from the N image quality evaluation values, to obtain K image quality evaluation values; K is an integer greater than 1 and less than or equal to N; Obtain a reference image corresponding to each of the K image quality evaluation values, to obtain K reference images; Classify the K reference images according to the algorithm type of the image enhancement processing algorithm, to obtain P reference image sets; each reference image set includes at least one reference image, and P is a positive integer less than K; Determine the target image enhancement processing algorithm and the target image enhancement algorithm control parameter according to the P reference image sets.

7. The system of claim 6, wherein, In the aspect of determining the target image enhancement processing algorithm and the target image enhancement algorithm control parameter according to the P reference image sets, the determining unit is specifically configured to: Determine an image quality evaluation mean value of the enhanced image of each reference image set in the P reference image sets, to obtain P image quality evaluation mean values; Perform weighted operation according to the P image quality evaluation mean values and the number of images in each reference image set in the P reference image sets, to obtain P image enhancement processing algorithm evaluation values; Select a maximum value in the P image enhancement processing algorithm evaluation values, and take the image enhancement processing algorithm corresponding to the maximum value as the target image enhancement processing algorithm; Determine the target image enhancement algorithm control parameter corresponding to the target image enhancement processing algorithm.

8. The system of claim 7, wherein, In the aspect of performing weighted operation according to the P image quality evaluation mean values and the number of images in each reference image set in the P reference image sets, to obtain P image enhancement processing algorithm evaluation values, the determining unit is specifically configured to: Obtain the number j of images corresponding to the reference image set corresponding to the image quality evaluation mean value i; the image quality evaluation mean value i is any image quality evaluation mean value in the P image quality evaluation mean values; Determine a target weight value corresponding to the number j of images; Perform weighted operation according to the target weight value and the image quality evaluation mean value i, to obtain a corresponding image enhancement processing algorithm evaluation value.

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