Image processing method and device, image controller and computer readable storage medium

Abstract

The invention provides an image processing method and device, an image controller and a computer readable storage medium, and relates to the ISP image processing field and the CIS field technology.The method comprises the steps that if it is determined that a preset sub-device meets preset collection rule information in the image collection process, a to-be-adjusted and optimized image is collected through the sub-device, and the to-be-adjusted and optimized image is obtained; and target detection is carried out on a preset target index in the to-be-optimized image, and an actual index value of the target index is determined. Loading a preset parameter mapping file; wherein the parameter mapping file comprises a plurality of image signal processing parameters in a preset range. And according to the actual index value and the parameter mapping file, performing parameter tuning processing on the to-be-tuned image to obtain a target image. According to the method provided by the invention, the image quality debugging efficiency can be improved, the image debugging quality is improved, and the technical problem that the image tuning efficiency is relatively low is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ISP image processing and CIS, and particularly relates to an image processing method and device, an image controller and a computer readable storage medium. BACKGROUND

[0002] At present, objective evaluation of image quality is a process of evaluating the quality of an image through quantitative methods and indicators. Such evaluation usually does not depend on human subjective feelings, but calculates various parameters of an image based on mathematical models and algorithms. In fact, the good or bad of objective indicators of image quality is closely related to various factors, such as image acquisition quality, ISP algorithm parameters, and environmental conditions.

[0003] In the prior art, when an image is optimized, the image is first captured, and then a tuning tool is used to adjust the light intensity of the image, the chart type, and the like.

[0004] However, in the prior art, the light intensity of the image, the chart type, and the like can only be adjusted in sequence by means of the tuning tool. If the shooting effect of the image is poor and the number of images is large, the optimization will be more time-consuming and laborious, the accuracy is not high enough, the optimization effect is poor, and the optimization efficiency is low. SUMMARY

[0005] The present application provides an image processing method, device, image controller and computer readable storage medium to solve the technical problem of low optimization efficiency of images.

[0006] In a first aspect, the present application provides an image processing method, comprising:

[0007] If it is determined in the image acquisition process that the preset sub-device meets the preset acquisition rule information, the image to be optimized is acquired through the sub-device, target detection is performed on the preset target indicator in the image to be optimized, and the actual indicator value of the target indicator is determined;

[0008] A preset parameter mapping file is loaded, wherein the parameter mapping file includes a plurality of image signal processing parameters in a preset range;

[0009] According to the actual indicator value and the parameter mapping file, parameter optimization processing is performed on the image to be optimized to obtain a target image.

[0010] Further, the parameter optimization processing on the image to be optimized according to the actual indicator value and the parameter mapping file to obtain a target image comprises:

[0011] For the actual index value to be optimized in the to-be-optimized image, among the plurality of image signal processing parameters in the range, determine, under the optimization of each image signal processing parameter, the actual index value after optimization as an optimization index value;

[0012] Determine the image signal processing parameter corresponding to the highest optimization index value as a target image signal processing parameter;

[0013] According to the target image signal processing parameter, perform parameter optimization processing on the to-be-optimized image to obtain a target image.

[0014] Further, the sub-device is connected with an image controller, and the image controller is configured to control the sub-device to collect images; the method further comprises:

[0015] In the initialization process of the image controller, load preset system configuration information and preset regulation and control scene configuration information of the image controller; wherein the system configuration information represents preset parameter configuration information of the image controller in a running process, and the system configuration information comprises a target index type and an environmental parameter constraint; the regulation and control scene configuration information represents initialization configuration information of the sub-device, and the regulation and control scene configuration information comprises an initial position value of the sub-device in a scene and an initial brightness value of the scene where the sub-device is located.

[0016] Further, perform target detection on a preset target index in the to-be-optimized image to determine an actual index value of the target index, comprising:

[0017] According to the target index type in the system configuration information, perform target detection on a target index corresponding to the target index type in the to-be-optimized image to determine an actual index value of the target index.

[0018] Further, the method further comprises:

[0019] In the image collection process, correct the sub-device by using the regulation and control scene configuration information and the environmental parameter constraint in the system configuration information to obtain correction result information;

[0020] If the correction result information represents that the current sub-device satisfies preset correction rule information, it is determined that the sub-device satisfies preset collection rule information.

[0021] Further, the correction of the sub-device by using the regulation and control scene configuration information and the environmental parameter constraint in the system configuration information in the image collection process to obtain correction result information comprises:

[0022] analyzing the initial brightness value in the regulation scene configuration information, if it is determined that the initial brightness value is outside the preset brightness range interval, regulating the initial brightness value until the regulated initial brightness value is determined to be within the preset brightness range interval;

[0023] analyzing the brightness consistency of the regulated initial brightness value, if it is determined that the brightness consistency meets the preset brightness threshold value, it is determined that the current sub-device meets the preset correction rule information;

[0024] If it is determined that the brightness consistency does not meet the brightness threshold value, the initial position value in the regulation scene configuration information is judged according to the environmental parameter constraint, if it is determined that the initial position value does not reach the preset correction boundary and the brightness consistency meets the brightness threshold value, it is determined that the current sub-device meets the preset correction rule information.

[0025] Further, the method further comprises:

[0026] outputting system logs and key data of each stage; wherein the system logs represent process logs generated in the image optimization process; the stage represents the time node of each optimization, and the key data includes the actual indicator value of the target indicator and the optimized optimization indicator value.

[0027] In a second aspect, the application provides an image processing device, comprising:

[0028] The acquisition module is configured to acquire a to-be-optimized image through the sub-device if it is determined that the preset sub-device meets the preset acquisition rule information in the image acquisition process.

[0029] The detection module is configured to perform target detection on the preset target indicator in the to-be-optimized image to determine the actual indicator value of the target indicator.

[0030] The loading module is configured to load a preset parameter mapping file; wherein the parameter mapping file includes a plurality of image signal processing parameters.

[0031] The optimization module is configured to perform parameter optimization processing on the to-be-optimized image according to the actual indicator value and the parameter mapping file to obtain a target image.

[0032] Further, the optimization module comprises:

[0033] The first determination unit is configured to determine, for the to-be-optimized actual indicator value in the to-be-optimized image, the optimized optimization indicator value of the actual indicator value under the optimization of each image signal processing parameter among the plurality of image signal processing parameters in the range.

[0034] The second determining unit is configured to determine an image signal processing parameter corresponding to the highest optimization index value as a target image signal processing parameter.

[0035] The optimization unit is configured to perform parameter optimization processing on the image to be optimized according to the target image signal processing parameter, to obtain a target image.

[0036] Further, the sub-device is connected with an image controller, and the image controller is configured to control the sub-device to collect images.

[0037] In the initialization process of the image controller, preset system configuration information and preset regulation and control scene configuration information of the image controller are loaded, wherein the system configuration information represents preset parameter configuration information of the image controller in a running process, and the system configuration information includes a target index type and an environmental parameter constraint; and the regulation and control scene configuration information represents initialization configuration information of the sub-device, and the regulation and control scene configuration information includes an initial position value of the sub-device in a scene and an initial brightness value of the scene where the sub-device is located.

[0038] Further, the detection module is specifically configured to:

[0039] According to the target index type in the system configuration information, a target index corresponding to the target index type in the image to be optimized is detected to determine an actual index value of the target index.

[0040] Further, the apparatus further includes:

[0041] The correction module is configured to perform correction processing on the sub-device according to the regulation and control scene configuration information and the environmental parameter constraint in the system configuration information, to obtain correction result information.

[0042] The determining module is configured to determine that the sub-device satisfies preset collection rule information if the correction result information represents that the current sub-device satisfies preset correction rule information.

[0043] Further, the correction module includes:

[0044] The first analysis unit is configured to analyze the initial brightness value in the regulation and control scene configuration information, and perform regulation and control on the initial brightness value until the regulated initial brightness value is located in a preset brightness range interval if it is determined that the initial brightness value is outside the preset brightness range interval.

[0045] The second analysis unit is configured to analyze the brightness consistency of the adjusted initial brightness value, and if it is determined that the brightness consistency meets a preset brightness threshold, it is determined that the current sub-device meets the preset correction rule information.

[0046] The third determination unit is configured to, if it is determined that the brightness consistency does not meet the brightness threshold, judge the initial position value in the regulation scene configuration information according to the environmental parameter constraint, and if it is determined that the initial position value does not reach a preset correction boundary and the brightness consistency meets the brightness threshold, it is determined that the current sub-device meets the preset correction rule information.

[0047] Further, the device further comprises:

[0048] The output module is configured to output system logs and key data of each stage, wherein the system logs represent process logs generated in the image optimization process, the stage represents a time node at which optimization is performed each time, and the key data includes an actual index value of the target index and an optimized optimization index value.

[0049] In a third aspect, the present application provides an image controller, comprising a memory and a processor, the memory stores a computer program executable on the processor, and the processor implements the method of the first aspect when executing the computer program.

[0050] In a fourth aspect, the present application provides a computer readable storage medium, the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method of the first aspect.

[0051] In a fifth aspect, the present application provides a computer program product, comprising a computer program, and the computer program is executed by the processor to implement the method of the first aspect.

[0052] The image processing method, device, image controller and computer readable storage medium provided by the present application, if the preset sub-device meets the preset collection rule information in the image collection process, the image to be optimized is collected through the sub-device, target detection is performed on the preset target index in the image to be optimized, and the actual index value of the target index is determined. Load the preset parameter mapping file; wherein the parameter mapping file includes a plurality of image signal processing parameters in a preset range. According to the actual index value and the parameter mapping file, the image to be optimized is processed by parameter optimization to obtain a target image. In the present scheme, when it is determined that the sub-device meets the preset collection rule information, the image to be optimized with good image collection quality is collected, target detection is performed on the preset target index in the image to be optimized, the actual index value of the target index is determined, and the image to be optimized is processed by parameter optimization according to the actual index value and the loaded parameter mapping file to obtain a target image. Therefore, the present application takes into account the influence of image collection quality, ISP algorithm parameter configuration, environmental adjustment and the like, can improve the efficiency of image quality debugging, improve the image debugging quality, and solve the technical problem of low image optimization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0053] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and serve to explain the principles of the present disclosure, together with the description.

[0054] Figure 1 A flowchart of an image processing method provided by an embodiment of the present application;

[0055] Figure 2 A flowchart of another image processing method provided by an embodiment of the present application;

[0056] Figure 3 A flowchart of another image processing method provided by an embodiment of the present application;

[0057] Figure 4 A flowchart of an image optimization method provided by an embodiment of the present application;

[0058] Figure 5 A structural diagram of an image processing device provided by an embodiment of the present application;

[0059] Figure 6 A structural diagram of another image processing device provided by an embodiment of the present application;

[0060] Figure 7 A structural diagram of an image controller provided by an embodiment of the present application;

[0061] Figure 8 A block diagram of an image controller provided by an embodiment of the present application.

[0062] The specific embodiments of the present disclosure have been shown by the above drawings, and will be described in more detail hereinafter. These drawings and the written description are not intended to restrict the scope of the concept of the present disclosure by any means, but to illustrate the concept of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0063] The exemplary embodiments will be described in detail herein with reference to the accompanying drawings. In the following description, the same numbers refer to the same elements throughout the drawings, unless otherwise represented. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present disclosure.

[0064] At present, the objective evaluation of image quality is a process of evaluating the quality of the image by quantitative methods and indicators. Such evaluation usually does not depend on human subjective feelings, but is based on mathematical models and algorithms to calculate various parameters of the image. In fact, the good or bad of the objective indicators of image quality is closely related to many factors, such as image acquisition quality, ISP algorithm parameters, and environmental conditions.

[0065] In an example, when the image is tuned, the image is usually first taken, and then the light adjustment intensity of the image, the card type and the like are adjusted by means of the tuning tool. However, in the prior art, since the light adjustment intensity of the image, the card type and the like can only be adjusted in sequence by means of the tuning tool, if the shooting effect of the image is poor and the number of images is large, the tuning will be more time-consuming and laborious, the accuracy is not high enough, resulting in poor tuning effect and low tuning efficiency.

[0066] The image processing method, device, image controller and computer readable storage medium provided by the present application aim to solve the above technical problems of the prior art.

[0067] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in detail in some embodiments. The embodiments of the present application will be described below with reference to the drawings.

[0068] Figure 1 A flowchart of an image processing method provided by an embodiment of the present application is shown in FIG. 1, which includes the following steps. Figure 1

[0069] Step 101: If it is determined in the image acquisition process that the preset sub-device meets the preset acquisition rule information, the image to be tuned is acquired by the sub-device, and the target index in the image to be tuned is detected to determine the actual index value of the target index.​

[0070] Exemplarily, the execution subject of the embodiment can be an image controller, or a terminal device, or an image processing device or equipment, or other devices or equipment capable of executing the embodiment, and no limitation is made thereto. In the embodiment, the execution subject is introduced as an image controller.

[0071] Firstly, the image controller is connected with a plurality of preset sub-devices, and the image controller is configured to control the sub-devices to collect images. The preset collection rule information refers to the collection conditions that the sub-devices need to meet, for example, the light intensity of the scene where the sub-devices are located, the initial position of the sub-devices, and the collection angle and collection boundary corresponding to the initial position, and no limitation is made thereto.

[0072] In this step, during the image collection process, it is determined whether the preset sub-devices meet the collection rule information. If it is determined that the sub-devices meet the collection rule information during the image collection process, the sub-devices are used to collect the images to be optimized, and target detection is performed on the preset target indicators in the images to be optimized to determine the actual indicator values of the target indicators. The preset target indicators can be one or more.

[0073] Step 102, loading a preset parameter mapping file; wherein the parameter mapping file includes a plurality of image signal processing parameters in a preset range.

[0074] Exemplarily, the preset parameter mapping file is a parameter configuration file preset by a user, the parameter mapping file is configured to optimize the images to be optimized, and the parameter mapping file includes a plurality of image signal processing (ISP) parameters. The preset parameter mapping file is loaded, and specifically, the parameter mapping file can be loaded in a soft link manner, so that the image controller obtains the ISP parameters in the parameter mapping file.

[0075] Step 103, performing parameter optimization processing on the images to be optimized according to the actual indicator values and the parameter mapping file to obtain target images.

[0076] Exemplarily, since the ISP parameters can affect the image quality, that is, different ISP parameters can affect the actual indicator values in the images to be optimized, each ISP parameter in the parameter mapping file is searched in sequence, the actual indicator values of the images to be optimized under the optimization of each ISP parameter are determined, the changed values are the optimized indicator values after optimization, and the ISP parameter corresponding to the maximum optimized indicator value is determined as the target ISP parameter. According to the target ISP parameter, the optimized target images are obtained, and therefore the actual indicator values of the target images at this time are the optimized indicator values of the best display quality.

[0077] For example, if the image to be optimized includes one actual index value, each ISP parameter in the plurality of ISP parameters in the parameter mapping file is searched in sequence to determine the target ISP parameter, and the optimized target image is obtained according to the target ISP parameter. Alternatively, if the image to be optimized includes a plurality of actual index values, one actual index value is obtained by normalizing the plurality of actual index values, each ISP parameter in the plurality of ISP parameters in the parameter mapping file is searched in sequence for the normalized actual index value to determine the target ISP parameter, and the optimized target image is obtained according to the target ISP parameter.

[0078] Therefore, the quality of the objective index of the image quality is closely related to various factors, such as image acquisition quality, ISP algorithm parameters, environmental conditions, and the like. In view of the above requirements, the present application can obtain a better optimization index value, target ISP parameter, and optimized target image according to the target ISP parameter by considering the modularity and integration and cooperating with the search strategy for searching the ISP parameter.

[0079] In the embodiment of the present application, if it is determined that the preset sub-device satisfies the preset acquisition rule information in the image acquisition process, the image to be optimized is acquired through the sub-device, target detection is performed on the preset target index in the image to be optimized to determine the actual index value of the target index, a preset parameter mapping file is loaded, the parameter mapping file includes a plurality of image signal processing parameters in a preset range, and the image to be optimized is subjected to parameter optimization processing according to the actual index value and the parameter mapping file to obtain a target image. In the present scheme, when it is determined that the sub-device satisfies the preset acquisition rule information, the image to be optimized with good image acquisition quality is acquired, target detection is performed on the preset target index in the image to be optimized to determine the actual index value of the target index, and the image to be optimized is subjected to parameter optimization processing according to the actual index value and the loaded parameter mapping file to obtain a target image. Therefore, the present application takes into account the influence of the image acquisition quality, ISP algorithm parameter configuration, environmental adjustment, and the like, can improve the efficiency of image quality debugging, improve the image debugging quality, and solve the technical problem of low optimization efficiency of the image.

[0080] Figure 2 Another flowchart of an image processing method provided by the embodiment of the present application is shown in FIG. 2, and the method includes the following steps. Figure 2

[0081] ​In step 201, the sub-device is connected with the image controller, and the image controller is configured to control the sub-device to collect images; in the initialization process of the image controller, preset system configuration information and preset regulation scene configuration information of the image controller are loaded; the system configuration information represents preset parameter configuration information of the image controller in the running process, and the system configuration information includes target index types and environmental parameter constraints; the regulation scene configuration information represents initialization configuration information of the sub-device, and the regulation scene configuration information includes an initial position value of a scene where the sub-device is located and an initial brightness value of the sub-device in the scene.

[0082] In an example, the image controller is connected with a plurality of preset sub-devices, and the image controller is configured to control the sub-devices to collect images. In the initialization process of the image controller, preset system configuration information and preset regulation scene configuration information of the image controller are loaded. The system configuration information represents preset parameter configuration information of the image controller in the running process, and the system configuration information includes target index types and environmental parameter constraints. Specifically, the target index types can be resolution, signal-to-noise ratio, etc. of image data, and the environmental parameter constraints include brightness range, brightness uniformity, etc. of the image data. The regulation scene configuration information represents initialization configuration information of the sub-devices, and the regulation scene configuration information includes an initial position value of the sub-device in the scene and an initial brightness value of the scene where the sub-device is located, and further includes configuration parameters of ISP chips in the sub-device, etc.

[0083] In step 202, in the image collection process, the sub-device is corrected according to the regulation scene configuration information and the environmental parameter constraints in the system configuration information, to obtain correction result information.

[0084] In an example, step 202 includes: analyzing the initial brightness value in the regulation scene configuration information, and if it is determined that the initial brightness value is outside a preset brightness range interval, the initial brightness value is regulated until the regulated initial brightness value is within the preset brightness range interval; analyzing the brightness consistency of the regulated initial brightness value, and if it is determined that the brightness consistency meets a preset brightness threshold, it is determined that the current sub-device meets preset correction rule information; if it is determined that the brightness consistency does not meet the brightness threshold, the initial position value in the regulation scene configuration information is judged according to the environmental parameter constraints, and if it is determined that the initial position value does not reach a preset correction boundary and the brightness consistency meets the brightness threshold, it is determined that the current sub-device meets the preset correction rule information.

[0085] In an example, in the image collection process, the sub-device needs to be corrected first. Figure 3 A flowchart of another image processing method provided by the embodiment of the present application is shown in FIG. 4. Figure 3As shown, first, the initial brightness value in the regulation scene configuration information is subjected to brightness mean value analysis, if it is determined that the initial brightness value is outside the preset brightness range interval, the initial brightness value is regulated until it is determined that the regulated initial brightness value is within the preset brightness range interval, the regulation is stopped. Then, the brightness consistency of the regulated initial brightness value is subjected to light disturbance analysis, if it is determined that the brightness consistency meets the preset brightness threshold, it is determined that the current sub-device meets the preset correction rule information, and the correction process is ended. If it is determined that the brightness consistency does not meet the brightness threshold, the initial position value in the regulation scene configuration information is continuously judged according to the environmental parameter constraint, if it is determined that the initial position value reaches the preset correction boundary, it is indicated that the current sub-device cannot be moved any more, i.e., the position cannot be continuously adjusted, and an abnormal information is returned; if it is determined that the initial position value does not reach the preset correction boundary, it is indicated that the position of the current sub-device can be continuously moved and adjusted, and the light disturbance analysis is repeatedly performed until it is determined that the brightness consistency meets the brightness threshold, and the correction process is ended.

[0086] Therefore, based on the brightness mean value analysis of the brightness and the light disturbance analysis of the color distribution, the external test environment is corrected, which can avoid the interference of abnormal data including glare / distracted light on the subsequent image acquisition.

[0087] Step 203, if the correction result information indicates that the current sub-device meets the preset correction rule information, it is determined that the sub-device meets the preset acquisition rule information.

[0088] Exemplarily, the preset correction rule information is the standard reference information of the correction result which is set in advance. If the correction result information indicates that the current sub-device meets the preset correction rule information, it is determined that the sub-device meets the preset acquisition rule information. Therefore, when the sub-device meets the preset acquisition rule information, the image is acquired, which can ensure the quality of the subsequently acquired image, for example, the brightness mean value, the brightness consistency, etc. in the image can be improved, and thus the ISP parameter tuning pressure in the next step can be reduced, so that when the ISP parameter tuning is performed on the image with higher quality, the target image with better tuning effect can be obtained.

[0089] Step 204, if it is determined in the image acquisition process that the preset sub-device meets the preset acquisition rule information, the sub-device is used to acquire the image to be tuned.

[0090] Exemplarily, if it is determined in the image acquisition process that the preset sub-device meets the preset acquisition rule information, the sub-device can be used to acquire the image to be tuned.

[0091] Step 205, according to the target index type in the system configuration information, a target index corresponding to the target index type in the image to be tuned is subjected to target detection, and the actual index value of the target index is determined.

[0092] For example, this step can be referred to Figure 1 Step 101 in the text will not be repeated here.

[0093] Step 206: Load the preset parameter mapping file; wherein the parameter mapping file includes multiple image signal processing parameters within a preset range.

[0094] For example, the preset parameter mapping file is a parameter configuration file pre-set by the user. The parameter mapping file is used to optimize the image to be optimized, and it includes multiple Image Signal Processing (ISP) parameters. In this step, the preset parameter mapping file is loaded. Specifically, the loading method can be to load the parameter mapping file through a symbolic link, so that the image controller can obtain the ISP parameters in the parameter mapping file.

[0095] Step 207: Based on the actual index values ​​and parameter mapping file, perform parameter tuning on the image to be tuned to obtain the target image.

[0096] In one example, step 207 includes: for the actual index value to be optimized in the image to be optimized, among multiple image signal processing parameters within a range, determining the optimized index value of the actual index value after optimization for each image signal processing parameter; determining the image signal processing parameter corresponding to the highest optimized index value as the target image signal processing parameter; and performing parameter optimization processing on the image to be optimized according to the target image signal processing parameter to obtain the target image.

[0097] For example, since ISP parameters can affect image quality, that is, different ISP parameters can affect the actual index value in the image to be optimized, each ISP parameter is searched sequentially among the multiple ISP parameters in the parameter mapping file to determine the actual index value of the image to be optimized after the change under the optimization of each ISP parameter. The changed value is the optimized index value, and the ISP parameter corresponding to the largest optimization index value is determined as the target ISP parameter. Based on the target ISP parameter, the image to be optimized is optimized to obtain the optimized target image. Therefore, the actual index value of the target image at this time is the optimization index value with the best display quality.

[0098] For example, if the image to be optimized includes a single actual metric value, then each ISP parameter in the parameter mapping file is searched sequentially to determine the target ISP parameter. Based on the target ISP parameter, the image to be optimized is then obtained, resulting in the optimized target image. Alternatively, if the image to be optimized includes multiple actual metric values, then these values ​​are normalized to obtain a normalized actual metric value. For this normalized actual metric value, each ISP parameter in the parameter mapping file is searched sequentially to determine the target ISP parameter. Based on the target ISP parameter, the image to be optimized is then obtained, resulting in the optimized target image.

[0099] Furthermore, the ISP parameters in the parameter mapping file include adjustment levels, i.e., priorities. Based on the parameter range and adjustment levels in the parameter mapping file, multi-parameter system optimization of the image to be optimized is performed. Among the multiple ISP parameters in the parameter mapping file, each ISP parameter is searched sequentially according to its adjustment level, from highest to lowest. This determines the actual index value of the image to be optimized under the adjustment of each ISP parameter. The changed value is the optimized index value, and the ISP parameter corresponding to the largest optimization index value is identified as the target ISP parameter. Based on the target ISP parameter, the image to be optimized is then optimized to obtain the optimized target image.

[0100] Step 208: Output system logs and key data for each stage; where the system logs represent the process logs generated during image optimization; the stages represent the time nodes for each optimization; and the key data include the actual values ​​of the target indicators and the optimized indicator values ​​after optimization.

[0101] For example, the image controller outputs system logs and key data for each stage. The system logs represent the process logs generated during the image optimization process of the image to be optimized; the stages represent the time points at which each optimization is performed; and the key data includes the actual value of the target indicator and the optimized indicator value after optimization.

[0102] In this embodiment, during the initialization of the image controller, preset system configuration information and preset control scene configuration information are loaded. The system configuration information represents the configuration information of preset parameters of the image controller during operation, including target index types and environmental parameter constraints. The control scene configuration information represents the initialization configuration information of the sub-device, including the initial position value of the sub-device in the scene and the initial brightness value of the sub-device in the scene. During image acquisition, the sub-device is corrected using the control scene configuration information and the environmental parameter constraints in the system configuration information to obtain correction result information. If the correction result information indicates that the current sub-device meets preset correction rule information, then the sub-device is determined to meet preset acquisition rule information. If the preset sub-device is determined to meet the preset acquisition rule information during image acquisition, the image to be optimized is acquired through the sub-device. Based on the target index type in the system configuration information, target detection is performed on the target index corresponding to the target index type in the image to be optimized to determine the actual index value of the target index. A preset parameter mapping file is loaded; wherein the parameter mapping file includes multiple image signal processing parameters within a preset range. Based on the actual index values ​​and parameter mapping file, parameter tuning is performed on the image to be tuned to obtain the target image. System logs and key data from each stage are output; the system logs represent the process logs generated during image optimization; each stage represents the time point of each tuning; and the key data includes the actual index values ​​of the target index and the tuned index values. Therefore, this application considers the impact of image acquisition quality, ISP algorithm parameter configuration, and environmental adjustment, which can improve the efficiency and quality of image quality tuning and solve the technical problem of low image tuning efficiency. Furthermore, secondary analysis of key data during the tuning process allows relevant personnel to have a more intuitive analysis of the degree of influence of each image parameter on the image output.

[0103] In one example, this application includes: a soft link module for ISP parameters, a front-end correction module, a dynamic search module, a configuration module, a calibration feature detection module, an analysis module, etc., wherein the front-end correction module includes a brightness control unit (i.e., a brightness mean analysis negative feedback system) and a sensor displacement control unit (i.e., a light disturbance analysis negative feedback system). Figure 4 This is a schematic diagram of the overall process of an image optimization method provided in an embodiment of this application, as shown below. Figure 4As shown, the specific process includes: configuring system configuration information and adjusting scene configuration information through the configuration module; loading the ISP mapping file, which refers to loading the parameter mapping file; linking ISP parameters, which refers to linking the ISP parameters in the parameter mapping file through the soft link module; performing brightness mean analysis through the brightness adjustment unit in the front-end correction module, performing light disturbance analysis and correction boundary analysis through the sensor displacement control unit; acquiring the image to be optimized through the acquisition system; performing target detection on the preset target index in the image to be optimized through the calibration feature detection module; obtaining the actual index value of the target index through the analysis module; sequentially searching each ISP parameter in the parameter mapping file through the dynamic search module to determine the actual index value of the image to be optimized after optimization under each ISP parameter optimization, and determining the target ISP parameter; updating the ISP parameter, which refers to updating the current ISP parameter to the target ISP parameter; determining whether the preset number of iterations has been reached or the error of the search engine is less than the preset threshold. If so, the result is output; otherwise, the search continues in the parameter mapping file through the domain control system.

[0104] In this embodiment, since the configuration of ISP parameters is directly related to the final image effect, a soft link unit for ISP parameters is introduced. This allows the image controller to obtain the ISP parameters from the parameter mapping file and optimize the image based on the ISP parameters, enabling a more comprehensive analysis of the image effect. In the actual calibration process, the data received by the Image Signal Processing unit (ISP RX) is not only affected by the sensor itself but also by the actual environment and installation status. Therefore, a dynamic correction unit is introduced to reduce the impact of external interference on the ISP input. Furthermore, the convergence process of the final target index is actually a non-linear process, equivalent to a constrained multi-parameter optimization problem. Therefore, a dynamic search unit is introduced to obtain better image quality more quickly.

[0105] In one example, this application provides an image optimization system, which includes an image controller and multiple sub-devices. The image controller is used to execute the methods described in the above embodiments.

[0106] Figure 5 This is a schematic diagram of the structure of an image processing apparatus provided in an embodiment of this application, as shown below. Figure 5 As shown, the device includes:

[0107] The acquisition module 31 is used to acquire the image to be optimized through the sub-device if it is determined during the image acquisition process that the preset sub-device meets the preset acquisition rule information.

[0108] The detection module 32 is used to perform target detection on the preset target indicators in the image to be optimized, and to determine the actual index value of the target indicators.

[0109] Loading module 33 is used to load a preset parameter mapping file; wherein the parameter mapping file includes multiple image signal processing parameters.

[0110] The optimization module 34 is used to perform parameter optimization processing on the image to be optimized based on the actual index values ​​and parameter mapping file to obtain the target image.

[0111] The apparatus in this embodiment can execute the technical solutions in the above method. Its specific implementation process and technical principles are the same, and will not be repeated here.

[0112] Figure 6 This is a schematic diagram of another image processing apparatus provided in an embodiment of this application. Figure 5 Based on the illustrated embodiments, as Figure 6 As shown, the tuning module 34 includes:

[0113] The first determining unit 341 is used to determine, among multiple image signal processing parameters within a range, the optimized index value of the actual index value in the image to be optimized, after optimization of each image signal processing parameter.

[0114] The second determining unit 342 is used to determine the image signal processing parameters corresponding to the highest optimization index value as the target image signal processing parameters.

[0115] The tuning unit 343 is used to perform parameter tuning on the image to be tuned based on the target image signal processing parameters to obtain the target image.

[0116] In one example, the sub-devices are connected to an image controller, which controls multiple sub-devices to acquire images; the device is also specifically used for:

[0117] During the initialization process of the image controller, the preset system configuration information and preset control scene configuration information of the image controller are loaded. The system configuration information represents the configuration information of the preset parameters of the image controller during operation, including the target index type and environmental parameter constraints. The control scene configuration information represents the initialization configuration information of the sub-device, including the initial position value of the sub-device in the scene and the initial brightness value of the scene in which the sub-device is located.

[0118] In one example, detection module 32 is specifically used for:

[0119] Based on the target indicator type in the system configuration information, target detection is performed on the target indicators in the image to be optimized that correspond to the target indicator type, and the actual indicator value of the target indicator is determined.

[0120] In one example, the device also includes:

[0121] The correction module 41 is used to perform correction processing on the sub-device by adjusting the scene configuration information and the environmental parameter constraints in the system configuration information during the image acquisition process, and to obtain correction result information.

[0122] The determination module 42 is used to determine that the sub-device meets the preset acquisition rule information if the correction result information indicates that the current sub-device meets the preset correction rule information.

[0123] In one example, correction module 41 includes:

[0124] The first analysis unit 411 is used to analyze the initial brightness value in the control scene configuration information. If it is determined that the initial brightness value is outside the preset brightness range, the initial brightness value is controlled until it is determined that the controlled initial brightness value is within the preset brightness range.

[0125] The second analysis unit 412 is used to analyze the brightness consistency of the initial brightness value after adjustment. If it is determined that the brightness consistency meets the preset brightness threshold, then it is determined that the current sub-device meets the preset correction rule information.

[0126] The third determining unit 413 is used to determine the initial position value in the control scene configuration information according to the environmental parameter constraints if the brightness consistency does not meet the brightness threshold. If the initial position value does not reach the preset correction boundary and the brightness consistency meets the brightness threshold, the current sub-device is determined to meet the preset correction rule information.

[0127] In one example, the device also includes:

[0128] Output module 43 is used to output system logs and key data for each stage; wherein, the system log represents the process log generated during the image optimization process; the stage represents the time node for each optimization; and the key data includes the actual index value of the target index and the optimized index value after optimization.

[0129] The apparatus in this embodiment can execute the technical solutions in the above method. Its specific implementation process and technical principles are the same, and will not be repeated here.

[0130] Figure 7 This is a schematic diagram of the structure of an image controller provided in an embodiment of this application, such as... Figure 7 As shown, the image controller includes: a memory 51 and a processor 52.

[0131] The memory 51 stores a computer program that can run on the processor 52.

[0132] Processor 52 is configured to perform the methods provided in the embodiments described above.

[0133] The image controller also includes a receiver 53 and a transmitter 54. The receiver 53 is used to receive instructions and data sent by external devices, and the transmitter 54 is used to send instructions and data to external devices.

[0134] Figure 8 This is a block diagram of an image controller provided in an embodiment of this application. The image controller can be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0135] The device 600 may include one or more of the following components: a processing component 602, a memory 604, a power supply component 606, a multimedia component 608, an audio component 610, an input / output (I / O) interface 612, a sensor component 614, and a communication component 616.

[0136] Processing component 602 typically controls the overall operation of device 600, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 602 may include one or more processors 620 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 602 may include one or more modules to facilitate interaction between processing component 602 and other components. For example, processing component 602 may include a multimedia module to facilitate interaction between multimedia component 608 and processing component 602.

[0137] Memory 604 is configured to store various types of data to support the operation of device 600. Examples of such data include instructions for any application or method operating on device 600, contact data, phonebook data, messages, pictures, videos, etc. Memory 604 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0138] Power supply component 606 provides power to the various components of device 600. Power supply component 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 600.

[0139] Multimedia component 608 includes a screen that provides an output interface between device 600 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 608 includes a front-facing camera and / or a rear-facing camera. When device 600 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0140] Audio component 610 is configured to output and / or input audio signals. For example, audio component 610 includes a microphone (MIC) configured to receive external audio signals when device 600 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 604 or transmitted via communication component 616. In some embodiments, audio component 610 also includes a speaker for outputting audio signals.

[0141] I / O interface 612 provides an interface between processing component 602 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0142] Sensor assembly 614 includes one or more sensors for providing state assessments of various aspects of device 600. For example, sensor assembly 614 may detect the on / off state of device 600, the relative positioning of components such as the display and keypad of device 600, changes in the position of device 600 or a component of device 600, the presence or absence of user contact with device 600, the orientation or acceleration / deceleration of device 600, and temperature changes of device 600. Sensor assembly 614 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 614 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 614 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0143] Communication component 616 is configured to facilitate wired or wireless communication between device 600 and other devices. Device 600 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 616 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 616 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0144] In an exemplary embodiment, the apparatus 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0145] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 604 including instructions, which can be executed by a processor 620 of the device 600 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0146] This application also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of the image controller, enables the image controller to perform the methods provided in the above embodiments.

[0147] This application also provides a computer program product, which includes: a computer program stored in a readable storage medium, at least one processor of the image controller can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the image controller to perform the scheme provided in any of the above embodiments.

[0148] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0149] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An image processing method, characterized by, The method comprises the following steps: If it is determined that the preset sub-device meets the preset acquisition rule information during image acquisition, the image to be optimized is acquired through the sub-device, target detection is performed on the preset target index in the image to be optimized, and the actual index value of the target index is determined; A preset parameter mapping file is loaded; wherein the parameter mapping file comprises a plurality of image signal processing parameters in a preset range; According to the actual index value and the parameter mapping file, parameter optimization processing is performed on the image to be optimized to obtain a target image.

2. The method of claim 1, wherein, The method according to the actual index value and the parameter mapping file, parameter optimization processing is performed on the image to be optimized to obtain a target image, comprising: For the actual index value to be optimized in the image to be optimized, in the plurality of image signal processing parameters in the range, the actual index value is determined in the optimization of each image signal processing parameter to obtain an optimized index value; The image signal processing parameter corresponding to the highest optimized index value is determined as a target image signal processing parameter; According to the target image signal processing parameter, parameter optimization processing is performed on the image to be optimized to obtain a target image.

3. The method of claim 1, wherein, The sub-device is connected with an image controller, and the image controller is used to control the sub-device to acquire images; the method further comprises: During the initialization process of the image controller, preset system configuration information and preset regulation and control scene configuration information of the image controller are loaded; wherein the system configuration information represents the configuration information of the preset parameters of the image controller during the running process, and the system configuration information comprises a target index type and an environmental parameter constraint; the regulation and control scene configuration information represents the initialization configuration information of the sub-device, and the regulation and control scene configuration information comprises an initial position value of the sub-device in a scene and an initial brightness value of the scene where the sub-device is located.

4. The method of claim 3, wherein, Target detection is performed on the preset target index in the image to be optimized to determine the actual index value of the target index, comprising: According to the target index type in the system configuration information, target detection is performed on the target index corresponding to the target index type in the image to be optimized to determine the actual index value of the target index.

5. The method of claim 3, wherein, The method further comprises: During the image acquisition process, the sub-device is corrected through the regulation and control scene configuration information and the environmental parameter constraint in the system configuration information to obtain correction result information; If the correction result information represents that the current sub-device meets the preset correction rule information, it is determined that the sub-device meets the preset acquisition rule information.

6. The method of claim 5, wherein, The method further comprises: The initial brightness value in the regulation and control scene configuration information is analyzed, and if it is determined that the initial brightness value is outside the preset brightness range interval, the initial brightness value is regulated until the regulated initial brightness value is located within the preset brightness range interval; The brightness consistency of the regulated initial brightness value is analyzed, and if it is determined that the brightness consistency meets a preset brightness threshold, it is determined that the current sub-device meets the preset correction rule information; If it is determined that the brightness consistency does not meet the brightness threshold, the initial position value in the regulation scene configuration information is judged according to the environmental parameter constraint, and if it is determined that the initial position value does not reach the preset correction boundary and the brightness consistency meets the brightness threshold, it is determined that the current sub-device meets the preset correction rule information.

7. The method according to any one of claims 1 to 6, characterized in that, The method further comprises: Outputting system logs and key data of each stage; wherein the system logs represent process logs generated in the image optimization process; the stage represents the time node of each optimization, and the key data includes the actual index value of the target index and the optimized optimization index value.

8. An image processing apparatus characterized by comprising: Including: The acquisition module is configured to acquire a to-be-optimized image through the sub-device if it is determined that the preset sub-device meets the preset acquisition rule information during the image acquisition process; The detection module is configured to perform target detection on the preset target index in the to-be-optimized image to determine the actual index value of the target index; The loading module is configured to load a preset parameter mapping file; wherein the parameter mapping file includes a plurality of image signal processing parameters; The optimization module is configured to perform parameter optimization processing on the to-be-optimized image according to the actual index value and the parameter mapping file to obtain a target image.

9. An image controller, characterized by, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method of any one of claims 1-7.

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