Automatic image exposure method and device, terminal and storage medium

Abstract

The invention provides an image automatic exposure method and device, a terminal and a storage medium. The image automatic exposure method comprises the following steps: acquiring a first image, a second image and exposure parameters of the first image and the second image; respectively carrying out a preset number of Gamma transformations on the first image and the second image to obtain a first type of images and a second type of images; determining brightness information entropies of the first type of images and the second type of images with the same transformation parameter and a first transformation parameter corresponding to the maximum brightness information entropy; determining a first update exposure based on the exposure parameter and the first transformation parameter of the first image, and determining a second update exposure based on the exposure parameter and the first transformation parameter of the second image; a joint exposure amount is determined based on the first updated exposure amount, the second updated exposure amount, a first brightness mean value of the first image, and a second brightness mean value of the second image. The method can improve the feature richness of input images of algorithms such as feature matching and depth estimation, and further improves the quality of the algorithms.

Description

Technical Field

[0001] This disclosure relates to the field of information technology, and in particular to methods, apparatus, terminals and storage media for automatic image exposure. Background Technology

[0002] Dual-camera systems, due to their wider field of view, superior low-light image quality, and ability to acquire additional depth information, have been widely used in mobile photography and video enhancement, as well as in virtual reality headsets for spatial perception. The brightness and brightness consistency of dual-camera images significantly impact the performance and effectiveness of image fusion enhancement algorithms and spatial perception and localization algorithms used in headsets. Currently, automatic exposure algorithms are employed to maintain brightness consistency. Existing automatic exposure algorithms statistically analyze the brightness characteristics of dual-camera images to calculate appropriate exposure parameters, resulting in a more consistent perceived brightness. Alternatively, they calculate exposure parameters by analyzing the statistical characteristics of the brightness in the shared viewing area of ​​the two cameras, making the automatic exposure results more consistent with human visual perception.

[0003] Exposure algorithms that use average image brightness as a control index are difficult to handle stable depth estimation in special scenarios such as high dynamic range scenes. In particular, when the spatial distribution of feature points in the scene is not uniform, there may be a situation where there are not many feature points even when the automatic exposure is stable, which will affect the accuracy of feature matching and depth estimation.

[0004] Furthermore, because the content of the dual-camera images acquired by the dual-camera system differs, the statistical characteristics of image brightness may vary, potentially leading to inconsistent brightness levels in the exposure parameters provided by the algorithm. Additionally, due to differences in the reflective properties of the target surface, the same target viewed by the dual cameras positioned on the left and right sides may exhibit different brightness levels, affecting the convergence of the automatic exposure parameters. All of these factors can cause problems in dual-camera image matching, thereby impacting the effectiveness of the algorithm. Summary of the Invention

[0005] To address the existing problems, this disclosure provides an automatic image exposure method, apparatus, terminal, and storage medium.

[0006] The following technical solution is adopted in this disclosure.

[0007] This disclosure provides an automatic image exposure method, comprising: acquiring a first image, a second image, and exposure parameters of the first and second images, wherein the first and second images are dual-camera images obtained by a dual-camera system; performing a preset number of Gamma transformations on the first and second images respectively to obtain a preset number of first-type images and a preset number of second-type images, wherein the first-type images include the first image and the second-type images include the second image; determining the brightness information entropy of the first-type images and the second-type images with the same transformation parameters, and a first transformation parameter corresponding to the maximum brightness information entropy; determining a first updated exposure amount based on the exposure parameters of the first image and the first transformation parameter, and determining a second updated exposure amount based on the exposure parameters of the second image and the first transformation parameter; and determining a joint exposure amount based on the first updated exposure amount, the second updated exposure amount, a first average brightness value of the first image, and a second average brightness value of the second image.

[0008] Another embodiment of this disclosure provides an automatic image exposure device. The processing device includes: an acquisition module configured to acquire a first image, a second image, and exposure parameters of the first image and the second image, wherein the first image and the second image are dual-camera images obtained by a dual-camera system; a transformation module configured to perform a preset number of Gamma transformations on the first image and the second image respectively to obtain the preset number of first-type images and the preset number of second-type images, wherein the first-type images include the first image and the second-type images include the second image; an information entropy determination module configured to determine the brightness information entropy of the first-type images and the second-type images with the same transformation parameters, and a first transformation parameter corresponding to the maximum brightness information entropy; an updated exposure determination module configured to determine a first updated exposure based on the exposure parameters of the first image and the first transformation parameter, and a second updated exposure based on the exposure parameters of the second image and the first transformation parameter; and a joint exposure determination module configured to determine a joint exposure based on the first updated exposure, the second updated exposure, a first average brightness value of the first image, and a second average brightness value of the second image.

[0009] In some embodiments, this disclosure provides a terminal, including: at least one memory and at least one processor; wherein the memory is used to store program code, and the processor is used to call the program code stored in the memory to execute the above-described automatic image exposure method.

[0010] In some embodiments, this disclosure provides a storage medium for storing program code for executing the above-described automatic image exposure method.

[0011] This disclosure uses luminance information entropy as a control index for automatic exposure, ensuring that the scene provides the richest amount of information when automatic exposure is stable. This improves the feature richness of the input image for algorithms such as feature matching and depth estimation, thereby enhancing the quality of the algorithms. This disclosure also uses the mean luminance as a weight for allocation, which is more stable than the information content weight and can prevent significant overexposure of one camera when another is obstructed, thus improving the stability of the exposure algorithm. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0013] Figure 1 This is a flowchart of an automatic image exposure method according to an embodiment of the present disclosure.

[0014] Figure 2 A schematic flowchart of an automatic image exposure method according to an embodiment of the present disclosure is shown.

[0015] Figure 3 This is a partial module of an automatic image exposure apparatus according to another embodiment of this disclosure.

[0016] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0017] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0018] It should be understood that the various steps described in the method embodiments of this disclosure can be performed in sequence and / or in parallel. Furthermore, method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0019] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0020] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0021] It should be noted that the use of the word "a" in this disclosure is illustrative rather than restrictive, and those skilled in the art should understand that it should be understood as "one or more" unless otherwise expressly indicated in the context.

[0022] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0023] Figure 1 A flowchart of an automatic image exposure method according to an embodiment of the present disclosure is provided. Figure 2 A schematic flowchart illustrating an automatic image exposure method according to an embodiment of the present disclosure is shown. The automatic image exposure method of the present disclosure may include step S101, acquiring a first image, a second image, and exposure parameters for the first and second images, wherein the first and second images are dual-camera images obtained by a dual-camera system. In some embodiments, the exposure parameters include exposure time and exposure gain. In some embodiments, the dual-camera system includes virtual reality glasses or an autonomous driving system. For example, taking virtual reality glasses as an example, the first image may be a left image or a left-eye image obtained by the left camera, and the second image may be a right image or a right-eye image obtained by the right camera.

[0024] In some embodiments, the method of this disclosure may further include step S102, performing a preset number of Gamma transformations on the first image and the second image respectively to obtain a preset number of first-class images and a preset number of second-class images. In some embodiments, the preset number can be determined based on the computational complexity of the algorithm; the larger the preset number, the higher the computational complexity of the algorithm. Typically, the preset number is 4-6. In some embodiments, the transformation parameters of the preset number of Gamma transformations include values ​​less than 1, values ​​equal to 1, and values ​​greater than 1. In some embodiments, the first-class image includes the first image, and the second-class image includes the second image, where the transformation parameter corresponds to 1. For example, assuming the preset number is 5, and the transformation parameters are 0.8, 0.9, 1.0, 1.1, and 1.2 respectively, then after Gamma transformation, a total of 10 images are obtained: 5 first-class images obtained by performing Gamma transformation on the first image, and 5 second-class images obtained by performing Gamma transformation on the second image, including the first image and the second image when the transformation parameter is 1.0 (a transformation parameter of 1.0 corresponds to no Gamma transformation). In some embodiments, the Gamma transformation formula is I. new =255*(I cur / 255) γ Of course, logarithmic or other forms of gamma transformation can also be used.

[0025] In some embodiments, the method of this disclosure may further include step S103, determining the luminance entropy of a first type of image and a second type of image with the same transformation parameter, and a first transformation parameter corresponding to the maximum luminance entropy. For example, determining the luminance entropy of a first type of image with a transformation parameter of 0.8 and a second type of image with a transformation parameter of 0.8, determining the luminance entropy of a first type of image with a transformation parameter of 0.9 and a second type of image with a transformation parameter of 0.9, etc. By determining their luminance entropy, the transformation parameter corresponding to the maximum luminance entropy (e.g., 0.9 or 1.1, etc.) can be obtained, and this transformation parameter is used as the first transformation parameter. In some embodiments, when determining the luminance entropy of an image, the frequency of each luminance from 0 to 255 (or downsampling from 256 sampling points to 64 or 32 points) in the image can be statistically analyzed, and the image information entropy can be calculated accordingly. The calculation formula is: Where H represents the joint image information entropy of the first type of image and the second type of image, i represents the pixel brightness value (range 0 to 255), and pi represents the frequency of the brightness i in the joint image.

[0026] In some embodiments, the method of this disclosure may further include step S104, determining a first updated exposure based on the exposure parameters and first transformation parameters of the first image, and determining a second updated exposure based on the exposure parameters and first transformation parameters of the second image. In some embodiments, determining the first updated exposure based on the exposure parameters and first transformation parameters of the first image, and determining the second updated exposure based on the exposure parameters and first transformation parameters of the second image, includes: determining the exposure of the first image based on the exposure parameters of the first image, determining the exposure of the second image based on the exposure parameters of the second image; determining the first updated exposure based on the exposure of the first image and the first transformation parameters, and determining the second updated exposure based on the exposure of the second image and the first transformation parameters. In some embodiments, the exposure of the first image or the exposure of the second image can be calculated using the following formula: EV cur t represents the current exposure of the first or second image. cur G represents the current exposure time of the first or second image. cur This indicates the current exposure gain of the first or second image.

[0027] In some embodiments, the first updated exposure or the second updated exposure (corresponding to) Figure 2 The updated gradient (in the equation) can be calculated using the following formula:

[0028] EV next =EV cur (1+αK p (R-1))

[0029] st R=d·tan{(2-γ opt )·arctan(1 / d)-arctan(1 / d)}+1

[0030] Among them, EV next Indicates the first or second update exposure, γ opt K represents the first transformation parameter. p 'a' is an adjustable parameter controlling the convergence speed, 'd' is an iteration control parameter, and K is an adjustable parameter controlling the convergence speed. p Both a and d can be set empirically. Typically, K... p The range is 0.2–0.5, d is 0.1, and a is 0.45–0.5 (when γ opt (≥1) or a is 1 (when γ) opt <1 hour).

[0031] In some embodiments, the method of this disclosure may further include step S105, determining a joint exposure based on a first updated exposure, a second updated exposure, a first average brightness of the first image, and a second average brightness of the second image. In some embodiments, it is assumed that the first updated exposure corresponding to the first image is EV. L The second updated exposure corresponding to the second image is EV. R The average brightness of the first image is A. L The average second brightness of the second image is A. R Then the combined exposure EV unite The following formula can be used for calculation: Then, the exposure values ​​of both cameras in the dual-camera system are set to this combined exposure value. Based on the preset exposure parameter table, the exposure parameters (i.e., exposure time and exposure gain) corresponding to this combined exposure value can be easily obtained.

[0032] Unlike existing average brightness control metrics, this disclosure uses brightness information entropy as the control metric for automatic exposure. This ensures that the scene provides the richest amount of information when automatic exposure is stable, improving the feature richness of the input image for algorithms such as feature matching and depth estimation, thereby enhancing the quality of the algorithms. Furthermore, joint exposure requires adjusting the binocular exposure parameters to be consistent to ensure similar brightness between the two cameras. However, if equal weights are used in weight allocation, one camera may be obscured while the other is overexposed; if information content weights are used, exposure non-convergence issues may occur. This disclosure uses the average brightness as the weight for allocation, which is more stable than information content weights and can prevent the problem of one camera being obscured while the other is significantly overexposed, thus improving the stability of the exposure algorithm.

[0033] In some embodiments, the automatic image exposure method of this disclosure further includes: determining a first average brightness value of a first image and a second average brightness value of a second image before determining a joint exposure amount. In some embodiments, the first average brightness value of the first image can be determined by traversing the first image, and the second average brightness value of the second image can be determined by traversing the second image.

[0034] In some embodiments, determining the luminance entropy of the first and second type images with the same transformation parameters includes: forming a predetermined number of joint images from the first and second type images with the same transformation parameters, and determining the luminance entropy of the joint images. For example, in some embodiments, a first type image with a transformation parameter of 0.8 and a second type image with a transformation parameter of 0.8 are combined into a joint image; a first type image with a transformation parameter of 0.9 and a second type image with a transformation parameter of 0.9 are combined into a joint image; a first type image with a transformation parameter of 1.0 and a second type image with a transformation parameter of 1.0 are combined into a joint image, and so on. It should be understood that the first and second type images can be combined into a joint image in either the width direction or the height direction. For example, taking transformation parameters of 0.8, 0.9, 1.0, 1.1, and 1.2 as examples, a total of 5 joint images are obtained. Then, the luminance entropy of these 5 joint images is determined.

[0035] In some embodiments, determining the first transformation parameter corresponding to the maximum luminance entropy includes: sorting the luminance entropies of the joint images to determine the maximum luminance entropy; and determining the transformation parameter of the joint image corresponding to the maximum luminance entropy as the first transformation parameter. For example, sorting the luminance entropies of the above 5 joint images, finding the transformation parameter corresponding to the joint image with the largest luminance entropy, and using this transformation parameter as the first transformation parameter.

[0036] In some embodiments, when the dual-camera images obtained by the dual-camera system undergo black level correction processing, determining the joint exposure based on the first updated exposure, the second updated exposure, the first average brightness of the first image, and the second average brightness of the second image includes: determining the joint exposure based on the first updated exposure, the second updated exposure, the first average brightness of the first image, the second average brightness of the second image, and the black level value. For example, assuming the black level value is BL, and the scaling factor β = (255 - BL) / 255, where BL is a positive value and much smaller than 255.

[0037] In some embodiments, the automatic image exposure method of this disclosure further includes: determining updated exposure parameters of the dual-camera system based on the joint exposure value. As described above, after determining the joint exposure value, both cameras of the dual-camera system are exposed according to the joint exposure value, and the exposure parameters (i.e., exposure time and exposure gain) corresponding to the joint exposure value can be easily obtained according to a preset exposure parameter table.

[0038] This disclosure predicts optimal image brightness by utilizing the luminance information entropy of the image, driving the image brightness towards locations with richer information. Furthermore, it uses luminance weights to control the joint exposure, improving joint exposure stability and ensuring consistent binocular exposure parameters without one eye being occluded while the other is overexposed. The active prediction exposure algorithm of this disclosure avoids the exposure oscillation effects of passive feedback-based exposure algorithms. Additionally, the automatic exposure method of this disclosure yields images with higher information content than automatic exposure algorithms based on luminance control indices, providing higher-quality input images for subsequent depth estimation and binocular vision algorithms. Moreover, the use of a luminance weighting method for joint exposure effectively prevents joint exposure non-convergence and avoids overexposure caused by joint exposure occlusion.

[0039] Embodiments of this disclosure also provide an automatic image exposure apparatus 400. Figure 3 An automatic image exposure apparatus 400 according to some embodiments is shown. The automatic image exposure apparatus 400 includes an acquisition module 401, a transformation module 402, an information entropy determination module 403, an updated exposure determination module 404, and a joint exposure determination module 405. In some embodiments, the acquisition module 401 is configured to acquire a first image, a second image, and exposure parameters of the first and second images, wherein the first and second images are dual-camera images obtained by a dual-camera system. In some embodiments, the transformation module 402 is configured to perform a preset number of Gamma transformations on the first and second images respectively, obtaining a preset number of first-type images and a preset number of second-type images, wherein the first-type images include the first image, and the second-type images include the second image. In some embodiments, the information entropy determination module 403 is configured to determine the brightness information entropy of the first-type and second-type images with the same transformation parameters, and a first transformation parameter corresponding to the maximum brightness information entropy. In some embodiments, the updated exposure determination module 404 is configured to determine a first updated exposure based on the exposure parameters of the first image and the first transformation parameter, and to determine a second updated exposure based on the exposure parameters of the second image and the first transformation parameter. In some embodiments, the joint exposure determination module 405 is configured to determine the joint exposure based on a first updated exposure, a second updated exposure, a first average brightness of a first image, and a second average brightness of a second image.

[0040] It should be understood that the description of the automatic image exposure method also applies to the automatic image exposure device 400 described herein, but for simplicity, it will not be described in detail here.

[0041] In some embodiments, the exposure parameters include exposure time and exposure gain, and the dual-camera system includes virtual reality glasses or an autonomous driving system. In some embodiments, the automatic image exposure device further includes: a brightness average determination module, configured to determine a first brightness average of a first image and a second brightness average of a second image before determining the joint exposure amount. In some embodiments, the transformation parameters of a preset number of Gamma transformations include values ​​less than 1, values ​​equal to 1, and values ​​greater than 1. In some embodiments, determining the brightness information entropy of a first type of image and a second type of image with the same transformation parameters includes: forming a preset number of joint images from the first type of image and the second type of image with the same transformation parameters, and determining the brightness information entropy of the joint images. In some embodiments, determining the first transformation parameter corresponding to the maximum brightness information entropy includes: sorting the brightness information entropy of the joint images to determine the maximum brightness information entropy; and determining the transformation parameter of the joint image corresponding to the maximum brightness information entropy as the first transformation parameter. In some embodiments, determining a first updated exposure based on the exposure parameters and first transformation parameters of the first image, and determining a second updated exposure based on the exposure parameters and first transformation parameters of the second image, includes: determining the exposure of the first image based on the exposure parameters of the first image, and determining the exposure of the second image based on the exposure parameters of the second image; determining the first updated exposure based on the exposure of the first image and the first transformation parameters, and determining the second updated exposure based on the exposure of the second image and the first transformation parameters. In some embodiments, when the dual-camera images obtained by the dual-camera system undergo black level correction processing, determining a joint exposure based on the first updated exposure, the second updated exposure, the first average brightness value of the first image, and the second average brightness value of the second image includes: determining the joint exposure based on the first updated exposure, the second updated exposure, the first average brightness value of the first image, the second average brightness value of the second image, and the black level value. In some embodiments, the automatic image exposure device further includes: an updated exposure parameter determination module, configured to determine the updated exposure parameters of the dual-camera system based on the joint exposure.

[0042] In addition, this disclosure also provides a terminal, including: at least one memory and at least one processor; wherein, the memory is used to store program code, and the processor is used to call the program code stored in the memory to execute the above-described automatic image exposure method.

[0043] In addition, this disclosure also provides a computer storage medium storing program code for executing the above-described automatic image exposure method.

[0044] The automatic image exposure method and apparatus of this disclosure have been described above based on embodiments and application examples. Furthermore, this disclosure also provides a terminal and a storage medium, which are described below.

[0045] The following is for reference. Figure 4 The diagram illustrates a structural schematic of an electronic device (e.g., a terminal device or a server) 500 suitable for implementing embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0046] like Figure 4 As shown, the electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0047] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0048] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0049] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0050] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0051] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0052] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods of the present disclosure.

[0053] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0054] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0055] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0056] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0057] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0058] According to one or more embodiments of this disclosure, an automatic image exposure method is provided, the automatic image exposure method comprising: acquiring a first image, a second image, and exposure parameters of the first image and the second image, wherein the first image and the second image are dual-camera images obtained by a dual-camera system; performing a preset number of Gamma transformations on the first image and the second image respectively to obtain the preset number of first-type images and the preset number of second-type images, wherein the first-type images include the first image and the second-type images include the second image; determining the brightness information entropy of the first-type images and the second-type images with the same transformation parameters and a first transformation parameter corresponding to the maximum brightness information entropy; determining a first updated exposure amount based on the exposure parameters of the first image and the first transformation parameter, and determining a second updated exposure amount based on the exposure parameters of the second image and the first transformation parameter; and determining a joint exposure amount based on the first updated exposure amount, the second updated exposure amount, a first average brightness value of the first image, and a second average brightness value of the second image.

[0059] According to one or more embodiments of this disclosure, the exposure parameters include exposure time and exposure gain, and the dual-camera system includes virtual reality glasses or an autonomous driving system.

[0060] According to one or more embodiments of this disclosure, the automatic image exposure method further includes: determining the first average brightness value of the first image and the second average brightness value of the second image before determining the joint exposure amount.

[0061] According to one or more embodiments of this disclosure, the preset number of Gamma transformation parameters include values ​​less than 1, values ​​equal to 1, and values ​​greater than 1.

[0062] According to one or more embodiments of this disclosure, determining the luminance information entropy of the first type of image and the second type of image with the same transformation parameters includes: forming the first type of image and the second type of image with the same transformation parameters into the preset number of joint images, and determining the luminance information entropy of the joint images.

[0063] According to one or more embodiments of this disclosure, determining the first transformation parameter corresponding to the maximum luminance information entropy includes: sorting the luminance information entropy of the joint image to determine the maximum luminance information entropy; and determining the transformation parameter of the joint image corresponding to the maximum luminance information entropy as the first transformation parameter.

[0064] According to one or more embodiments of this disclosure, determining a first updated exposure based on the exposure parameters of the first image and the first transformation parameters, and determining a second updated exposure based on the exposure parameters of the second image and the first transformation parameters, includes: determining the exposure of the first image based on the exposure parameters of the first image, determining the exposure of the second image based on the exposure parameters of the second image; determining the first updated exposure based on the exposure of the first image and the first transformation parameters, and determining the second updated exposure based on the exposure of the second image and the first transformation parameters.

[0065] According to one or more embodiments of this disclosure, when the dual-camera image obtained by the dual-camera system undergoes black level correction processing, determining the joint exposure based on the first updated exposure, the second updated exposure, the first average brightness of the first image, and the second average brightness of the second image includes: determining the joint exposure based on the first updated exposure, the second updated exposure, the first average brightness of the first image, the second average brightness of the second image, and the black level value.

[0066] According to one or more embodiments of this disclosure, the automatic image exposure method further includes: determining updated exposure parameters of the dual-camera system based on the combined exposure.

[0067] According to one or more embodiments of this disclosure, an automatic image exposure device is provided, the automatic image exposure device comprising: an acquisition module configured to acquire a first image, a second image, and exposure parameters of the first image and the second image, wherein the first image and the second image are dual-camera images obtained by a dual-camera system; a transformation module configured to perform a preset number of Gamma transformations on the first image and the second image respectively to obtain the preset number of first-type images and the preset number of second-type images, wherein the first-type images include the first image and the second-type images include the second image; an information entropy determination module configured to determine the brightness information entropy of the first-type images and the second-type images with the same transformation parameters and a first transformation parameter corresponding to the maximum brightness information entropy; an updated exposure determination module configured to determine a first updated exposure based on the exposure parameters of the first image and the first transformation parameter, and a second updated exposure based on the exposure parameters of the second image and the first transformation parameter; and a joint exposure determination module configured to determine a joint exposure based on the first updated exposure, the second updated exposure, a first average brightness of the first image, and a second average brightness of the second image.

[0068] According to one or more embodiments of the present disclosure, a terminal is provided, comprising: at least one memory and at least one processor; wherein the at least one memory is used to store program code, and the at least one processor is used to invoke the program code stored in the at least one memory to execute the method described in any one of the above descriptions.

[0069] According to one or more embodiments of the present disclosure, a storage medium is provided for storing program code for performing the methods described above.

[0070] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0071] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0072] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. An automatic exposure method of an image, characterized by, The image automatic exposure method comprises: obtaining a first image, a second image and exposure parameters of the first image and the second image, wherein the first image and the second image are dual-camera images obtained by a dual-camera system; performing a preset number of Gamma transformations on the first image and the second image respectively to obtain a preset number of first-type images and a preset number of second-type images, wherein the first-type images include the first image, and the second-type images include the second image; determining luminance information entropy of the first-type images and the second-type images with the same transformation parameter and a first transformation parameter corresponding to a maximum luminance information entropy; determining a first updated exposure based on the exposure parameter of the first image and the first transformation parameter, and determining a second updated exposure based on the exposure parameter of the second image and the first transformation parameter; determining a joint exposure based on the first updated exposure, the second updated exposure, a first luminance average of the first image and a second luminance average of the second image.

2. The automatic image exposure method according to claim 1, characterized in that, The exposure parameter comprises an exposure time and an exposure gain, and the dual-camera system comprises a virtual reality glasses or an unmanned driving system.

3. The method of claim 1, wherein, Further comprising: determining the first luminance average of the first image and the second luminance average of the second image before determining the joint exposure.

4. The method of claim 1, wherein, The transformation parameter of the preset number of Gamma transformations comprises a value less than 1, a value equal to 1 and a value greater than 1.

5. The method of claim 1, wherein, Determining the luminance information entropy of the first-type images and the second-type images with the same transformation parameter comprises: grouping the first-type images and the second-type images with the same transformation parameter to obtain a preset number of joint images; determining the luminance information entropy of the joint images.

6. The method of automatic exposure of an image according to claim 5, characterized in that, Determining the first transformation parameter corresponding to the maximum luminance information entropy comprises: sorting the luminance information entropy of the joint images to determine the maximum luminance information entropy; determining the transformation parameter of the joint image corresponding to the maximum luminance information entropy as the first transformation parameter.

7. The method of claim 1, wherein, Determining a first updated exposure based on the exposure parameter of the first image and the first transformation parameter, and determining a second updated exposure based on the exposure parameter of the second image and the first transformation parameter comprises: determining the exposure of the first image based on the exposure parameter of the first image, and determining the exposure of the second image based on the exposure parameter of the second image; determining the first updated exposure based on the exposure of the first image and the first transformation parameter, and determining the second updated exposure based on the exposure of the second image and the first transformation parameter.

8. The method of claim 1, wherein, When the dual-camera images obtained by the dual-camera system are subjected to black level correction processing, determining a joint exposure based on the first updated exposure, the second updated exposure, a first luminance average of the first image and a second luminance average of the second image comprises: determining the joint exposure based on the first updated exposure, the second updated exposure, the first luminance average of the first image, the second luminance average of the second image and a black level value.

9. The method of claim 1, wherein, Further comprising: determine an updated exposure parameter of the dual-camera system based on the joint exposure amount.

10. An automatic image exposure device, characterized by comprising: The image automatic exposure apparatus comprises: an acquisition module configured to acquire a first image, a second image, and exposure parameters of the first image and the second image, wherein the first image and the second image are dual-camera images obtained by a dual-camera system; a transformation module configured to perform a preset number of Gamma transformations on the first image and the second image respectively to obtain a preset number of first-type images and a preset number of second-type images, the first-type images including the first image, and the second-type images including the second image; an information entropy determination module configured to determine luminance information entropy of the first-type images and the second-type images with the same transformation parameter and a first transformation parameter corresponding to a maximum luminance information entropy; an updated exposure amount determination module configured to determine a first updated exposure amount based on the exposure parameter of the first image and the first transformation parameter, and determine a second updated exposure amount based on the exposure parameter of the second image and the first transformation parameter; a joint exposure amount determination module configured to determine a joint exposure amount based on the first updated exposure amount, the second updated exposure amount, a first luminance average of the first image, and a second luminance average of the second image.

11. A terminal comprising: at least one memory and at least one processor; wherein the at least one memory is configured to store program code, and the at least one processor is configured to invoke the program code stored in the at least one memory to execute the image automatic exposure method of any one of claims 1 to 9.

12. A storage medium configured to store program code, the program code being configured to execute the image automatic exposure method of any one of claims 1 to 9.

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