Automatic exposure convergence method and device, computer equipment and storage medium

Abstract

The invention belongs to the technical field of image processing, and relates to an automatic exposure convergence method, which comprises the following steps: receiving an acquired current environment illumination brightness signal, and calculating a current environment photosensitive value according to the illumination brightness signal; judging the illumination scene type; calling a corresponding preset matching algorithm according to the illumination scene type, and matching the current environment photosensitive value with a pre-constructed mapping table to obtain an initial exposure parameter; acquiring an initial image acquired based on the initial exposure parameter, and determining the current image brightness according to the initial image; and calculating a brightness error between the current image brightness and a preset target brightness, performing iterative fine tuning on the initial exposure parameter based on the brightness error, and stopping iteration and generating a stable image until exposure convergence or reaching a preset number of iterations. The invention further provides an automatic exposure convergence device, computer equipment and a storage medium. The automatic exposure convergence speed can be improved, power consumption is reduced, and meanwhile the stability of image quality is kept.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, and in particular to an automatic exposure convergence method and device, computer equipment and a storage medium. BACKGROUND

[0002] Automatic exposure technology is crucial in photography and imaging, as it directly affects the brightness, clarity, and overall quality of images. In dynamic scenes and complex lighting environments, fast and stable exposure control becomes a core requirement for achieving high-quality imaging. With the widespread use of intelligent devices and surveillance systems, automatic exposure needs to adapt quickly to various lighting conditions to capture clear and stable images. However, existing methods often struggle to meet both real-time and stability requirements when dealing with rapidly changing lighting scenarios, making it important to research more efficient exposure control methods.

[0003] Currently, automatic exposure control methods have significant limitations when dealing with complex lighting environments. Many traditional solutions rely on fixed exposure parameters or simple brightness adjustment logic, making it difficult to handle scenes with rapidly changing light intensity. For example, in low-light environments, repeated exposure parameter adjustments are required for 5-10 frames of images, resulting in convergence times exceeding 300ms. Such long convergence times significantly impact the device's response speed in scenarios requiring fast snapshots, making it difficult to capture key frames in time and meet real-time requirements for practical applications. High-frame-rate accelerated exposure can speed up exposure convergence to some extent, but at the cost of image quality, resulting in increased image noise and increased power consumption. High dynamic range automatic exposure control schemes use multi-frame fusion processing to optimize exposure effects, but the processing time exceeds 500ms, making them unsuitable for real-time snapshot requirements.

[0004] In summary, existing automatic exposure control schemes have deficiencies in convergence speed, image quality, power consumption, and environmental adaptability, making it difficult to meet the practical needs of scenarios that require fast automatic exposure convergence and low power consumption, such as hunting cameras, outdoor security surveillance devices, and wildlife observation instruments. SUMMARY

[0005] The purpose of the embodiments of the present application is to provide an automatic exposure convergence method, device, computer equipment, and storage medium to address the technical problem that existing automatic exposure control schemes have deficiencies in convergence speed, image quality, power consumption, and environmental adaptability, making it difficult to meet the practical needs of scenarios that require fast automatic exposure convergence and low power consumption.

[0006] In a first aspect, an automatic exposure convergence method is provided, which employs the following technical solution: receiving a current ambient light brightness signal collected, calculating a current ambient light sensitivity value according to the light brightness signal; determining a light scene type; calling a corresponding preset matching algorithm according to the light scene type, matching the current ambient light sensitivity value with a pre-constructed mapping table to obtain an initial exposure parameter, wherein the mapping table is used to represent a corresponding relationship between a light sensitivity value and an exposure parameter; obtaining an initial image collected based on the initial exposure parameter, and determining a current image brightness according to the initial image; calculating a brightness error between the current image brightness and a preset target brightness, and performing iterative fine-tuning on the initial exposure parameter based on the brightness error until exposure converges or a preset iteration number is reached, stopping iteration and generating a stable image.

[0007] In a second aspect, an automatic exposure convergence device is provided, which adopts the technical scheme as follows: The obtaining module is configured to receive a current ambient light brightness signal collected, and calculate a current ambient light sensitivity value according to the light brightness signal; The determining module is configured to determine a light scene type; The parameter matching module is configured to call a corresponding preset matching algorithm according to the light scene type, match the current ambient light sensitivity value with a pre-constructed mapping table to obtain an initial exposure parameter, wherein the mapping table is used to represent a corresponding relationship between a light sensitivity value and an exposure parameter; The image collecting module is configured to obtain an initial image collected based on the initial exposure parameter, and determine a current image brightness according to the initial image; The iteration module is configured to calculate a brightness error between the current image brightness and a preset target brightness, and perform iterative fine-tuning on the initial exposure parameter based on the brightness error until exposure converges or a preset iteration number is reached, stop iteration and generate a stable image.

[0008] In a third aspect, a computer device is provided, which adopts the technical scheme as follows: The computer device includes a memory and a processor, the memory stores computer readable instructions, and the processor implements the steps of the automatic exposure convergence method as described above when executing the computer readable instructions.

[0009] In a fourth aspect, a computer readable storage medium is provided, which adopts the technical scheme as follows: The computer readable storage medium stores computer readable instructions, and the computer readable instructions are executed by a processor to implement the steps of the automatic exposure convergence method as described above.

[0010] Compared with the prior art, the present application has the following beneficial effects: The present application provides an automatic exposure convergence method, which realizes dynamic self-adaptive fast and accurate exposure through the steps of perceiving ambient light brightness, determining light scene type, matching initial exposure parameters and iterative fine-tuning, is suitable for exposure under various light environments, improves automatic exposure convergence speed, reduces power consumption, maintains image quality stability at the same time, improves exposure efficiency and exposure effect, better meets the scenes with strict requirements for automatic exposure convergence speed and low power consumption such as hunting cameras, outdoor security monitoring devices, wild animal observation instruments and the like. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the scheme in the present application, the drawings needed in the description of the embodiments of the present application will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0012] Figure 1 is an exemplary system architecture diagram in which the present application can be applied; Figure 2 is a flowchart of one embodiment of the automatic exposure convergence method according to the present application; Figure 3 is a flowchart of one specific implementation of step S202 in Figure 2 Figure 4 is a flowchart of one specific implementation of step S203 in Figure 2 Figure 5 is a structural schematic diagram of one embodiment of the automatic exposure convergence device according to the present application; Figure 6 is a structural schematic diagram of one embodiment of the computer device according to the present application. DETAILED DESCRIPTION

[0013] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs; the terms used in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit the present application; the specification, claims and above-described drawing of the present application and the terms "include" and "have" and any variations thereof in the specification and claims of the present application are intended to cover non-exclusive inclusion. The terms "first", "second" and the like in the specification and claims of the present application or above-described drawings are used to distinguish different objects, not to describe a particular order.

[0014] ​​Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combined with other embodiments.

[0015] In order to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings.

[0016] As shown in Figure 1 The system architecture 100 can include a terminal device 101, a network 102, and a server 103. The terminal device 101 can be a notebook computer 1011, a tablet computer 1012, or a mobile phone 1013. The network 102 is a medium for providing a communication link between the terminal device 101 and the server 103. The network 102 can include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0017] A user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0018] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the notebook computer 1011, the tablet computer 1012, or the mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player, an MP4 player, a laptop computer, and a desktop computer, etc.

[0019] The server 103 can be a server providing various services, such as a background server supporting a page displayed on the terminal device 101.

[0020] It should be noted that the automatic exposure convergence method provided in the embodiments of the present application is generally executed by a server / terminal device, and accordingly, the automatic exposure convergence apparatus is generally arranged in the server / terminal device.

[0021] It should be understood that,Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0022] Continue to refer to Figure 2 The flowchart illustrates an embodiment of the automatic exposure convergence method according to this application, including the following steps: Step S201: Receive the collected current ambient light intensity signal and calculate the current ambient light sensitivity value based on the light intensity signal.

[0023] In this embodiment, the ambient light intensity signal is acquired using a photosensitive sensor. The photosensitive sensor typically uses a silicon photodiode or photoresistor as the photosensitive element. When ambient light shines on the surface of the photosensitive element, a change in current or resistance proportional to the light intensity is generated. The photosensitive sensor converts this current or resistance change into a voltage signal, which is then processed by an analog-to-digital converter to form a digitized ambient light intensity signal.

[0024] Specifically, a photosensor collects the ambient light intensity signal, and a signal amplification circuit amplifies the signal to obtain an enhanced electrical signal. An analog-to-digital converter then converts this enhanced electrical signal into a digital signal, which is transmitted to the main control chip. The main control chip processes the digital signal to obtain the current ambient light sensitivity value.

[0025] The signal amplification circuit typically includes operational amplifiers and feedback resistor networks. This circuit design can amplify weak raw electrical signals to a suitable amplitude level for subsequent processing without introducing excessive distortion. For example, in low-light environments, the raw illumination signal may only be in the millivolt range. An adjustable-gain amplification circuit can boost it to the volt range, ensuring the accuracy of subsequent conversion. After amplification, the enhanced electrical signal retains the waveform characteristics of the original illumination signal. The analog-to-digital converter (ADC) employs a successive approximation or Σ-Δ architecture, enabling high-resolution conversion of the analog signal into a digital signal. After conversion, the main control chip's digital signal processor performs averaging or median filtering on the digital signal to eliminate transient fluctuations. A calibration algorithm, based on the response characteristic curve of the photosensitive sensor, converts the filtered digital signal into a standard photosensitive value, mapping the digital signal to the corresponding photosensitive value.

[0026] In some alternative implementations, the step of calculating the current ambient light sensitivity value based on the light intensity signal is further included by preprocessing the light intensity signal.

[0027] The preprocessing process includes signal filtering and linearization correction. Signal filtering uses a low-pass filter to remove high-frequency noise interference. The cutoff frequency of the filter is determined based on the lighting characteristics of the application scenario. For example, for indoor environments, the cutoff frequency is typically set to 10 Hz to 50 Hz to filter out flicker interference from artificial lighting. For outdoor environments, the cutoff frequency can be appropriately increased to 100 Hz to retain information about natural lighting changes such as cloud cover.

[0028] Noise suppression is achieved through multiple sampling averaging. Multiple illumination samples are continuously collected within a short period, and a stable measurement value is obtained through weighted averaging. The weighting strategy considers the recentity of the sampling time, giving higher weights to newer samples to ensure that the measurement results reflect the true changes in ambient illumination in a timely manner.

[0029] Linearization correction is used to compensate for the nonlinear response characteristics of photosensors. The response curves of photosensors under different light intensities may exhibit nonlinear regions, especially under extremely low and high light conditions. The correction process uses a pre-established correction table or polynomial fitting function to convert the sensor's raw output into a standardized value that is linearly related to the actual light intensity.

[0030] In some alternative implementations, after the step of calculating the current ambient light sensitivity value based on the illuminance signal, the method further includes: color temperature compensation for the current ambient light sensitivity value.

[0031] Light sources with different color temperatures may produce different perceived brightness under the same light intensity. A color temperature sensor can be used to obtain the color temperature value of the current ambient light. Based on this color temperature value, a pre-built color temperature compensation table is used to correct the current ambient light sensitivity value. The color temperature compensation table represents the mapping relationship between color temperature values ​​and light sensitivity compensation coefficients. The corresponding light sensitivity compensation coefficient is obtained from the color temperature compensation table based on the color temperature value. Multiplying the current ambient light sensitivity value by the light sensitivity compensation coefficient yields the corrected light sensitivity value.

[0032] Step S202: Determine the lighting scene type.

[0033] Determining the type of lighting scene is a crucial step in automatic exposure control, directly impacting the selection of subsequent matching algorithms and the accuracy of exposure parameters. Different lighting scenes exhibit different characteristics of lighting variations, requiring corresponding processing strategies. For example, complex lighting scenes typically refer to environments with drastic changes in light intensity, diverse light source types, or strong contrasts between light and shadow, such as alternating sunlight and shadow outdoors or mixed lighting from multiple indoor light sources. Non-complex lighting scenes, on the other hand, refer to environments with relatively stable and slowly changing lighting, such as uniform indoor lighting or outdoor lighting on a cloudy day.

[0034] In some alternative implementations, see [link to relevant documentation]. Figure 3As shown, the steps for determining the lighting scene type include: Step S301: Obtain the rate of change of the current ambient light sensitivity value and compare the rate of change with a preset threshold.

[0035] The rate of change is calculated by the difference in photosensitivity values ​​between adjacent sampling points. Specifically, the photosensitivity value of the current sampling point is subtracted from the photosensitivity value of the previous sampling point, and then divided by the sampling time interval. To reduce the influence of single-point noise, a moving average or weighted average method is typically used to smooth the rate of change. The size of the moving average window is determined based on the temporal characteristics of the illumination change, and a window length of 3 to 10 sampling points is generally chosen.

[0036] Furthermore, the step of obtaining the rate of change of the current ambient photosensitivity value and comparing the rate of change with a preset threshold includes: The ambient light is continuously collected using a preset sampling frequency to obtain a photosensitive value sequence; the photosensitive value sequence is then smoothed to obtain a smoothed photosensitive value sequence; the difference in photosensitive values ​​between adjacent time points is calculated using the smoothed photosensitive value sequence to obtain a rate of change sequence; and all rates of change in the rate of change sequence are compared with a preset threshold.

[0037] The selection of the sampling frequency needs to be determined based on the characteristics of light changes in the environment. For example, in an indoor environment, where light changes are relatively slow, the sampling frequency can be set between 10 Hz and 20 Hz; in an outdoor environment, due to factors such as cloud cover and vehicle shadows, light changes may be more drastic, requiring a higher sampling frequency of 50 Hz to 100 Hz.

[0038] The photosensitivity value sequence is stored using a circular buffer structure. The buffer size is determined by calculating the required length of historical data based on the rate of change. When the buffer is full, new sampled data overwrites the oldest data, ensuring that the latest photosensitivity value sequence is always maintained. The data in the buffer is sorted by timestamp, and each data point contains the photosensitivity value and the corresponding sampling time information.

[0039] The purpose of data smoothing is to remove random noise and short-term fluctuations from the light sensitivity value sequence and extract the main trend of light intensity changes. Data smoothing can be achieved using digital filtering techniques, with common methods including moving average filtering, exponential smoothing filtering, and Kalman filtering. Moving average filtering achieves smoothing by calculating the average value of data points within a fixed window, the window size of which is determined based on noise levels and response speed requirements. Exponential smoothing filtering uses an exponentially decaying weighting method, assigning higher weights to recent data and lower weights to historical data. The choice of smoothing coefficient needs to strike a balance between noise suppression and response speed, and the coefficient value is usually between 0.1 and 0.9.

[0040] The calculation of the rate of change sequence is based on a smoothed photosensitive value sequence. For each sampling point in the photosensitive value sequence, the difference in photosensitive value between it and the previous sampling point is calculated, and divided by the time interval to obtain the instantaneous rate of change. Since the sampling frequency is fixed, the time interval is the reciprocal of the sampling period; the unit of the rate of change is usually the photosensitive value per second, such as lux per second (Lux / s).

[0041] The preset threshold is determined based on statistical analysis of a large number of real-world scenarios, taking into account the relative changes under different lighting levels. For example, in low-light environments, even small absolute changes may have a significant impact on image quality, so the preset threshold is relatively low; in high-light environments, the preset threshold can be relatively high because the human eye and image sensor are more adaptable.

[0042] Determining the type of lighting scene by calculating the rate of change of the current ambient light sensitivity value can improve the efficiency and accuracy of lighting scene judgment, so as to obtain accurate exposure parameters in the future.

[0043] Step S302: When the rate of change is greater than or equal to a preset threshold, the current environment is determined to be a complex lighting scene.

[0044] If at least one rate of change in the rate of change sequence is greater than or equal to a preset threshold, then the current environment is determined to be a complex lighting scene.

[0045] In some alternative implementations, the determination of complex lighting scenes needs to consider the persistence of the rate of change. A single high rate of change may be caused by noise in the photosensor or by chance and should not be classified as a complex lighting scene. Therefore, it is necessary to continuously monitor the rate of change within a certain time window, and only when the rate of change of multiple consecutive sampling points exceeds a preset threshold should it be classified as a complex lighting scene.

[0046] Step S303: If the rate of change is less than the preset threshold, the current environment is determined to be a non-complex lighting scene.

[0047] The characteristics of a non-complex lighting scene are that the lighting changes relatively slowly and steadily, with the rate of change consistently below a preset threshold. Specifically, if all rates of change in the rate of change sequence are less than the preset threshold, the current environment is determined to be a non-complex lighting scene.

[0048] In some optional implementations, a lag mechanism is introduced into the decision-making logic to avoid frequent switching of lighting scene types. Specifically, when the current lighting scene type is a complex lighting scene, the change rate needs to remain below a preset threshold for a certain period of time before switching to a non-complex lighting scene. Similarly, when the current lighting scene type is a non-complex lighting scene, the change rate needs to remain above a preset threshold for a certain period of time before switching to a complex lighting scene. The lag time can be set to 2 to 10 seconds to avoid jitter in the lighting scene type.

[0049] By comparing the rate of change of the current ambient light sensitivity value with a preset threshold, most complex lighting scenes can be effectively identified, improving the accuracy of lighting scene recognition.

[0050] Step S203: According to the lighting scene type, call the corresponding preset matching algorithm to match the current ambient light sensitivity value with the pre-built mapping table to obtain the initial exposure parameters.

[0051] Different lighting scenarios require different matching algorithms to obtain the optimal initial exposure parameters. For complex lighting scenarios, where lighting changes drastically and unpredictably, highly adaptable matching algorithms, such as scene-weighted algorithms, are needed. For non-complex lighting scenarios, where lighting is relatively stable, computationally simple and fast-responding matching algorithms, such as nearest neighbor matching algorithms or interval interpolation methods, can be used.

[0052] By invoking a preset matching algorithm corresponding to the lighting scene type, the current ambient light sensitivity value is matched with a pre-built mapping table to obtain the initial exposure parameters. The mapping table represents the correspondence between the light sensitivity value and the optimal exposure parameters, which include, but are not limited to, exposure time and gain. Correspondingly, the initial exposure parameters include the optimal initial exposure time and the optimal initial gain.

[0053] Optimal exposure parameters refer to the combination of exposure time and sensor gain that achieves the best balance between brightness distribution, contrast, and noise level in an image at a specific photosensitivity value (Lux). Specific criteria for determining "optimal" include: (1) Brightness target: The average brightness value of the image is within the preset target brightness range (e.g., within 10%). (2) Contrast preservation: The local contrast of the image (calculated by grayscale gradient) is not lower than the preset contrast threshold; (3) Signal-to-noise ratio (SNR): In low-light environments, the gain should not be too high to avoid introducing obvious noise; (4) Dynamic range: Highlights are not overexposed (RGB value < 250), and shadows have detail (RGB value > 5).

[0054] The optimal exposure parameter is determined by using a clustering algorithm to classify multiple sets of exposure parameters under similar lighting conditions, and taking the cluster center as the optimal exposure parameter for that lighting range.

[0055] It should be understood that the exposure amount can be determined by the product of the exposure time and the gain. In this embodiment, the gain refers to the amplification factor of the signal output by the image sensor.

[0056] Step S204: Obtain the initial image based on the initial exposure parameters, and determine the brightness of the current image based on the initial image.

[0057] In this embodiment, the image sensor sets the corresponding key parameters such as exposure time (shutter speed), aperture size, and ISO according to the initial exposure parameters to complete image acquisition, and calculates the current image brightness based on the acquired initial image.

[0058] In some alternative implementations, the steps for determining the brightness of the current image based on the initial image include: Extract the pixel brightness values ​​of the initial image; calculate the average brightness value of the initial image based on the pixel brightness values, and use the average brightness value as the brightness of the current image.

[0059] Extracting pixel brightness values ​​requires considering the image's color space and bit depth. Regarding color space, for images in the RGB color space, brightness values ​​are typically calculated using a weighted average. The weighting coefficients are determined based on the human eye's sensitivity to different colors; for example, the weight of the red component is approximately 0.299, the green component approximately 0.587, and the blue component approximately 0.114. For images in the YUV color space, brightness information is directly contained in the Y component, requiring no additional conversion calculations.

[0060] Regarding bit depth, for 8-bit images, the range of pixel brightness values ​​is 0 to 255; for 16-bit images, the range is 0 to 65535. Images with different bit depths need to be normalized to unify the brightness values ​​to the same range for easier subsequent processing and comparison.

[0061] In this embodiment, the average brightness value can be calculated using either a weighted average or a median method. Specifically, the weighted average method assigns different weights to pixels based on their position or importance in the image. For example, a Gaussian distribution model can be used, with the highest weight in the central region and the weight gradually decreasing towards the edge regions. The median method represents the overall brightness level of the image by calculating the median of the brightness values ​​of all pixels.

[0062] By using the average brightness value as the current image brightness, the overall brightness of the image can be comprehensively reflected, improving the accuracy of exposure adjustment.

[0063] Step S205: Calculate the brightness error between the current image brightness and the preset target brightness, perform iterative fine-tuning of the initial exposure parameters based on the brightness error, until the exposure converges or the preset number of iterations is reached, stop the iteration and generate a stable image.

[0064] Brightness error represents the difference between the target brightness and the current image brightness. Calculating brightness error is a crucial step in automatic exposure convergence control, determining the direction and magnitude of subsequent iterative adjustments. The target brightness is typically preset based on the application scenario and user needs, representing the desired image brightness level. The standard for setting the target brightness varies across different application scenarios.

[0065] In some alternative implementations, the steps described above for iteratively fine-tuning the initial exposure parameters based on brightness error until exposure convergence or a preset number of iterations is reached include: The absolute value of the brightness error is compared with a preset error threshold. When the absolute value of the error is less than the preset error threshold, the exposure is determined to have converged, and the iteration stops. When the absolute value of the error is greater than or equal to the preset error threshold, an exposure compensation factor is generated based on the brightness error, and the initial exposure parameters are adjusted based on the exposure compensation factor to obtain optimized exposure parameters. A new image is re-acquired based on the optimized exposure parameters, and the brightness error is recalculated on the new image to obtain a new absolute value of the error. The next iteration is triggered based on the new absolute value of the error, until the exposure converges or the preset number of iterations is reached.

[0066] The absolute value of the error is obtained by taking the absolute value of the brightness error between the current image brightness and the preset target brightness.

[0067] In one possible implementation, the preset error threshold can be set according to the percentage of the target brightness. This relative threshold setting method can adapt to the accuracy requirements under different target brightness and maintain appropriate convergence accuracy in both high-brightness and low-brightness scenarios.

[0068] Once exposure convergence is confirmed, the current exposure parameters will be locked, and the stability monitoring mode will be activated. In stability monitoring mode, the device continues to collect ambient light information, but does not immediately adjust the exposure parameters.

[0069] The exposure compensation factor is obtained by establishing a mapping relationship between brightness error and exposure parameter adjustment. Specifically, the brightness error is input, the corresponding exposure parameter adjustment is obtained and output, and the output exposure parameter adjustment is the exposure compensation factor.

[0070] The exposure compensation factor is determined based on the magnitude and direction of the brightness error. The initial exposure parameters are then adjusted according to their priority order, based on the exposure compensation factor. Specifically, a positive brightness error indicates that the current image brightness is lower than the target brightness, requiring an increase in exposure time and / or gain; a negative brightness error indicates that the current image brightness is higher than the target brightness, requiring a decrease in exposure time and / or gain. Adjusting the exposure time has the least negative impact on image quality, therefore it is the first parameter adjusted. When the exposure time reaches its upper or lower limit, the gain value is then adjusted.

[0071] Acquiring a new image requires waiting for the image sensor and processing circuit to stabilize. After parameter adjustment, several sampling cycles are waited to ensure the sensor output is stable before image acquisition. The image brightness and brightness error are recalculated for the acquired new image to obtain a new absolute value of the error between the new image brightness and the target brightness. Based on this new absolute value of the error, it is determined whether to trigger the next iteration.

[0072] In this embodiment, limiting the number of iterations is to prevent the system from falling into an infinite loop. The preset number of iterations is determined based on the application's real-time and quality requirements. In one embodiment, the preset number of iterations is set to 3.

[0073] In some optional implementations, the trend of the change between the absolute value of the new error and the absolute value of the previous error is analyzed. If the absolute value of the new error is less than the absolute value of the previous error, it indicates that the optimization direction is correct and the automatic exposure is converging towards the target state. If the absolute value of the new error is greater than or equal to the absolute value of the previous error, there may be two situations: one is that the error shows an oscillating pattern, indicating that the adjustment range may be too large and the exposure compensation factor needs to be reduced; the other is that the error shows a diverging trend, indicating that there may be a problem with the exposure parameter settings and recalibration is required.

[0074] The decision to trigger the next iteration needs to comprehensively consider factors such as the magnitude of the absolute error, the number of iterations, and stability. Specifically, if the new absolute error value is less than a preset error threshold, the next iteration will not be triggered; if the number of iterations reaches a preset number, the next iteration will not be triggered; if the new absolute error value does not show significant improvement, or if the new absolute error value exhibits periodic oscillations, the iteration will be terminated early to avoid wasting computational resources.

[0075] When the convergence condition or the maximum number of iterations is reached, the final stable image is acquired using the exposure parameters obtained from the last optimization. The main control chip converts the format of the stable image to generate an output image, which is then transmitted to a storage medium for storage.

[0076] In some optional implementations, the mapping table is dynamically updated based on the quality assessment results of the stabilized image. The quality assessment metrics include image brightness, contrast, saturation, and sharpness. These metrics are comprehensively evaluated during the generation of the stabilized image to ensure that the final stabilized image achieves the expected results in all aspects.

[0077] In some optional implementations, the exposure convergence process is automatically restarted when a significant change in lighting conditions is detected. The detection of lighting changes is based on real-time monitoring of ambient light sensitivity values. When the change in light sensitivity value exceeds a preset threshold, it is determined that the lighting conditions have changed and the exposure needs to be readjusted. This dynamic response mechanism ensures that the system can adapt to constantly changing shooting environments.

[0078] This application achieves dynamic adaptive fast and accurate exposure through steps such as sensing ambient light intensity, determining the type of lighting scene, matching initial exposure parameters, and iterative fine-tuning. It is applicable to exposure in various lighting environments, improves the automatic exposure convergence speed, reduces power consumption, and maintains the stability of image quality. It improves exposure efficiency and exposure effect, and better meets the stringent requirements of hunting cameras, outdoor security monitoring equipment, wildlife observation instruments, and other scenarios with strict requirements for automatic exposure convergence speed and low power consumption.

[0079] In some alternative implementations, see [link to relevant documentation]. Figure 4 As shown, the steps described above, which involve calling the corresponding preset matching algorithm based on the lighting scene type to match the current ambient light sensitivity value with a pre-built mapping table to obtain the initial exposure parameters, include: Step S401: When the current environment is a complex lighting scene, a scene weighting algorithm is used to match the current ambient light sensitivity value with a pre-built mapping table to obtain the initial exposure parameters.

[0080] Scene weighting algorithm is a matching strategy specifically designed for complex lighting scenes. Specifically, it obtains a sequence of historical photosensitivity values ​​collected within a preset time window; extracts statistical features of the historical photosensitivity value sequence; dynamically determines a weight allocation strategy based on the statistical features; assigns corresponding weights to multiple mapping entries in the mapping table according to the weight allocation strategy; and calculates the initial exposure parameters by weighting the photosensitivity values ​​using the assigned weights.

[0081] The historical photosensitivity value sequence is cached using timestamp indexing to ensure accurate tracking of the acquisition time of each data point. The preset time window is the most recent preset time window, and its length is determined based on the time scale of scene illumination changes. For example, for scenes with rapidly changing illumination, a shorter time window is set to maintain timely response; for scenes with relatively slow but unstable illumination changes, a longer time window is set to obtain more statistical information.

[0082] Statistical characteristics include the series mean, series variance, trend, series skewness, and series kurtosis. The series mean reflects the average level of recent lighting conditions, used to determine whether the current environment falls into the low, medium, or high illuminance range. The series variance reflects the drastic nature of light changes; high variance indicates unstable lighting conditions. The trend value, obtained through linear regression analysis, reflects the overall direction of light intensity change; an upward trend indicates the environment is brightening, while a downward trend indicates the environment is darkening, allowing for advance preparation for upcoming light changes. The series skewness reflects the symmetry of the photosensitive value distribution; positive skewness indicates a higher concentration of high photosensitive values, while negative skewness indicates a higher concentration of low photosensitive values. The series kurtosis reflects the sharpness of the distribution; high kurtosis indicates that photosensitive values ​​are concentrated within a certain range, while low kurtosis indicates a more dispersed distribution.

[0083] The weighting strategy prioritizes mapping entries that match the current lighting statistics. Specifically, when lighting shows a rapid upward trend, mapping entries corresponding to high lighting intensities are assigned higher weights; when lighting changes drastically, multiple exposure parameter combinations with faster response times are selected, and a weighted average is used to obtain more robust exposure parameters, which are the final initial exposure parameters. Here, a mapping entry refers to an independent mapping unit in the mapping table, that is, the exposure parameter corresponding to a specific photosensitivity value.

[0084] By dynamically adjusting the weights of different exposure parameters in the mapping table using a scene-weighted algorithm, more stable and accurate exposure parameters can be obtained.

[0085] Step S402: When the current environment is a non-complex lighting scene, an interpolation algorithm is used to match the current ambient light sensitivity value with a pre-built mapping table to obtain the initial exposure parameters.

[0086] For non-complex lighting scenes, since the environment is relatively stable, a more direct and efficient interpolation algorithm can be used for matching.

[0087] Interpolation algorithms include nearest neighbor matching and interval interpolation. The nearest neighbor matching algorithm calculates the distance between the current photosensitive value and each mapping entry in the mapping table, and selects the mapping entry with the smallest distance as the matching result. The distance calculation can use Euclidean distance or Manhattan distance, which has low computational complexity and fast response speed.

[0088] Interval interpolation is suitable when the current photosensitivity value lies between two adjacent entries in the mapping table. This method first determines the interval containing the current photosensitivity value, and then performs linear or spline interpolation between the exposure parameters corresponding to the endpoints of the interval. Linear interpolation is suitable for intervals where exposure parameters change relatively smoothly, while spline interpolation is suitable for intervals where exposure parameters change more complexly. The specific interpolation algorithm can be chosen based on the requirements for accuracy and response speed.

[0089] This application ensures the accuracy of exposure parameters and improves exposure convergence speed by employing appropriate matching algorithms based on different lighting scenarios.

[0090] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0091] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0092] Further reference Figure 5 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of an automatic exposure convergence device, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0093] like Figure 5 As shown, the automatic exposure convergence device 500 described in this embodiment includes: an acquisition module 501, a determination module 502, a parameter matching module 503, an image acquisition module 504, and an iteration module 505. Wherein: The acquisition module 501 is used to receive the collected current ambient light intensity signal and calculate the current ambient light sensitivity value based on the light intensity signal; The determination module 502 is used to determine the lighting scene type; The parameter matching module 503 is used to call the corresponding preset matching algorithm according to the lighting scene type, match the current ambient photosensitivity value with the pre-built mapping table, and obtain the initial exposure parameters. The mapping table is used to represent the correspondence between photosensitivity value and exposure parameters. The image acquisition module 504 is used to acquire an initial image based on the initial exposure parameters and determine the current image brightness based on the initial image; The iteration module 505 is used to calculate the brightness error between the current image brightness and the preset target brightness, and to perform iterative fine-tuning of the initial exposure parameters based on the brightness error until the exposure converges or the preset number of iterations is reached, then the iteration stops and a stable image is generated.

[0094] The aforementioned automatic exposure convergence device 500 achieves dynamic adaptive fast and accurate exposure through steps such as sensing ambient light intensity, determining the type of lighting scene, matching initial exposure parameters, and iterative fine-tuning. It is suitable for exposure in various lighting environments, improves the speed of automatic exposure convergence, reduces power consumption, and maintains the stability of image quality, thereby improving exposure efficiency and effect. It better meets the stringent requirements of hunting cameras, outdoor security monitoring equipment, wildlife observation instruments, and other scenarios with strict requirements for automatic exposure convergence speed and low power consumption.

[0095] In some optional implementations, the determination module 502 includes: The comparison submodule is used to obtain the rate of change of the current ambient photosensitivity value and compare the rate of change with a preset threshold. The first determination submodule is used to determine that the current environment is a complex lighting scene when the rate of change is greater than or equal to the preset threshold. The second determination submodule is used to determine that the current environment is a non-complex lighting scene when the rate of change is less than the preset threshold.

[0096] By comparing the rate of change of the current ambient light sensitivity value with a preset threshold, most complex lighting scenes can be effectively identified, improving the accuracy of lighting scene recognition.

[0097] In some optional implementations of this embodiment, the comparison submodule is further used for: The ambient light is continuously collected using a preset sampling frequency to obtain a photosensitive value sequence; The photosensitivity value sequence is smoothed to obtain a smoothed photosensitivity value sequence. The difference in photosensitivity values ​​between adjacent time points is calculated using the smoothed photosensitivity value sequence to obtain the rate of change sequence; The entire rate of change in the rate of change sequence is compared with the preset threshold.

[0098] Determining the type of lighting scene by calculating the rate of change of the current ambient light sensitivity value can improve the efficiency and accuracy of lighting scene judgment, so as to obtain accurate exposure parameters in the future.

[0099] In some alternative implementations, the parameter matching module 503 includes: The complex scene matching submodule is used to match the current environment photosensitivity value with a pre-built mapping table using a scene weighting algorithm when the current environment is a complex lighting scene, so as to obtain the initial exposure parameters; The non-complex scene matching submodule is used to match the current environment photosensitivity value with a pre-built mapping table using an interpolation algorithm when the current environment is a non-complex lighting scene, in order to obtain the initial exposure parameters.

[0100] By employing appropriate matching algorithms based on different lighting scenarios, the accuracy of exposure parameters can be ensured, thereby improving the exposure convergence speed.

[0101] In some optional implementations of this embodiment, the complex scene matching submodule includes: The acquisition unit is used to acquire a sequence of historical photosensitivity values ​​collected within a preset time window; An extraction unit is used to extract the statistical features of the historical photosensitivity value sequence; The weight determination unit is used to dynamically determine the weight allocation strategy based on the statistical characteristics. A weight allocation unit is used to allocate corresponding weights to multiple mapping entries in the mapping table according to the weight allocation strategy. The calculation unit is used to calculate the initial exposure parameters by performing a weighted average of the photosensitivity values ​​using the assigned weights.

[0102] By dynamically adjusting the weights of different exposure parameters in the mapping table using a scene-weighted algorithm, more stable and accurate exposure parameters can be obtained.

[0103] In some alternative implementations, the image acquisition module 504 includes: A brightness extraction submodule is used to extract the pixel brightness values ​​of the initial image; The brightness calculation submodule is used to calculate the average brightness value of the initial image based on the pixel brightness value, and use the average brightness value as the current image brightness.

[0104] By using the average brightness value as the current image brightness, the overall brightness of the image can be comprehensively reflected, improving the accuracy of exposure adjustment.

[0105] In some alternative implementations, the iteration module 505 is further used for: The absolute value of the brightness error is compared with a preset error threshold. When the absolute value of the error is less than the preset error threshold, the exposure is determined to have converged, and the iteration stops. When the absolute value of the error is greater than or equal to the preset error threshold, an exposure compensation factor is generated based on the brightness error, and the initial exposure parameters are adjusted based on the exposure compensation factor to obtain optimized exposure parameters; A new image is re-acquired based on the optimized exposure parameters, and the brightness error is recalculated on the new image to obtain a new absolute value of the error. The next iteration is triggered based on the new absolute value of the error until the exposure converges or the preset number of iterations is reached.

[0106] By performing a limited number of iterative optimizations to the exposure parameters, the efficiency and stability of exposure convergence can be significantly improved.

[0107] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0108] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected via a system bus. It should be noted that only the computer device 6 with memory 61, processor 62, and network interface 63 is shown in the figure; however, it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0109] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0110] The memory 61 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 6. Of course, the memory 61 may also include both the internal storage unit and its external storage device of the computer device 6. In this embodiment, the memory 61 is typically used to store the operating system and various application software installed on the computer device 6, such as computer-readable instructions for automatic exposure convergence methods. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or will be output.

[0111] In some embodiments, the processor 62 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to execute computer-readable instructions stored in the memory 61 or to process data, for example, to execute computer-readable instructions for the automatic exposure convergence method.

[0112] The network interface 63 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 6 and other electronic devices.

[0113] By sensing ambient light intensity, determining the type of lighting scene, matching initial exposure parameters, and iterative fine-tuning, dynamic adaptive fast and accurate exposure is achieved. It is suitable for exposure in various lighting environments, improves automatic exposure convergence speed, reduces power consumption, and maintains image quality stability, thereby improving exposure efficiency and effect. It better meets the stringent requirements of hunting cameras, outdoor security monitoring equipment, wildlife observation instruments, and other scenarios with strict requirements for automatic exposure convergence speed and low power consumption.

[0114] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the automatic exposure convergence method as described above.

[0115] By sensing ambient light intensity, determining the type of lighting scene, matching initial exposure parameters, and iterative fine-tuning, dynamic adaptive fast and accurate exposure is achieved. It is suitable for exposure in various lighting environments, improves automatic exposure convergence speed, reduces power consumption, and maintains image quality stability, thereby improving exposure efficiency and effect. It better meets the stringent requirements of hunting cameras, outdoor security monitoring equipment, wildlife observation instruments, and other scenarios with strict requirements for automatic exposure convergence speed and low power consumption.

[0116] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0117] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. An automatic exposure convergence method, characterized in that, Includes the following steps: Receive the collected current ambient light intensity signal and calculate the current ambient light sensitivity value based on the light intensity signal; Determine the type of lighting scene; According to the lighting scene type, the corresponding preset matching algorithm is invoked to match the current ambient photosensitivity value with a pre-built mapping table to obtain the initial exposure parameters. The mapping table is used to represent the correspondence between photosensitivity value and exposure parameters. Acquire an initial image based on the initial exposure parameters, and determine the current image brightness based on the initial image; Calculate the brightness error between the current image brightness and the preset target brightness, and perform iterative fine-tuning of the initial exposure parameters based on the brightness error until the exposure converges or the preset number of iterations is reached, then stop the iteration and generate a stable image.

2. The automatic exposure convergence method according to claim 1, characterized in that, The steps for determining the type of lighting scene include: Obtain the rate of change of the current ambient photosensitivity value, and compare the rate of change with a preset threshold; When the rate of change is greater than or equal to the preset threshold, the current environment is determined to be a complex lighting scene; If the rate of change is less than the preset threshold, the current environment is determined to be a non-complex lighting scene.

3. The automatic exposure convergence method according to claim 2, characterized in that, The step of obtaining the rate of change of the current ambient photosensitivity value and comparing the rate of change with a preset threshold includes: The ambient light is continuously collected using a preset sampling frequency to obtain a photosensitive value sequence; The photosensitivity value sequence is smoothed to obtain a smoothed photosensitivity value sequence. The difference in photosensitivity values ​​between adjacent time points is calculated using the smoothed photosensitivity value sequence to obtain the rate of change sequence; The entire rate of change in the rate of change sequence is compared with the preset threshold.

4. The automatic exposure convergence method according to claim 2, characterized in that, The step of calling the corresponding preset matching algorithm according to the lighting scene type to match the current ambient light sensitivity value with a pre-built mapping table to obtain the initial exposure parameters includes: When the current environment is a complex lighting scene, a scene weighting algorithm is used to match the current environment photosensitivity value with a pre-built mapping table to obtain the initial exposure parameters; When the current environment is a non-complex lighting scene, an interpolation algorithm is used to match the current environment photosensitivity value with a pre-built mapping table to obtain the initial exposure parameters.

5. The automatic exposure convergence method according to claim 4, characterized in that, The step of matching the current ambient light sensitivity value with a pre-built mapping table using a scene-weighted algorithm to obtain the initial exposure parameters includes: Obtain the historical photosensitivity value sequence collected within a preset time window; Extract the statistical features of the historical photosensitivity value sequence; Based on the aforementioned statistical characteristics, a weight allocation strategy is dynamically determined; According to the weight allocation strategy, assign corresponding weights to multiple mapping entries in the mapping table; The initial exposure parameters are obtained by calculating the weighted average of the photosensitivity values ​​after the weights are assigned.

6. The automatic exposure convergence method according to claim 1, characterized in that, The step of determining the brightness of the current image based on the initial image includes: Extract the pixel brightness values ​​of the initial image; The average brightness value of the initial image is calculated based on the pixel brightness value, and the average brightness value is used as the brightness of the current image.

7. The automatic exposure convergence method according to claim 1, characterized in that, The step of iteratively fine-tuning the initial exposure parameters based on the brightness error until the exposure converges or a preset number of iterations is reached includes: The absolute value of the brightness error is compared with a preset error threshold. When the absolute value of the error is less than the preset error threshold, the exposure is determined to have converged, and the iteration stops. When the absolute value of the error is greater than or equal to the preset error threshold, an exposure compensation factor is generated based on the brightness error, and the initial exposure parameters are adjusted based on the exposure compensation factor to obtain optimized exposure parameters; A new image is re-acquired based on the optimized exposure parameters, and the brightness error is recalculated on the new image to obtain a new absolute value of the error. The next iteration is triggered based on the new absolute value of the error until the exposure converges or the preset number of iterations is reached.

8. An automatic exposure convergence device, characterized in that, include: The acquisition module is used to receive the collected current ambient light intensity signal and calculate the current ambient light sensitivity value based on the light intensity signal; The determination module is used to determine the type of lighting scene; The parameter matching module is used to call the corresponding preset matching algorithm according to the lighting scene type, match the current ambient photosensitivity value with the pre-built mapping table to obtain the initial exposure parameters, wherein the mapping table is used to represent the correspondence between photosensitivity value and exposure parameters; An image acquisition module is used to acquire an initial image based on the initial exposure parameters and determine the current image brightness based on the initial image. The iteration module is used to calculate the brightness error between the current image brightness and the preset target brightness, and to perform iterative fine-tuning of the initial exposure parameters based on the brightness error until the exposure converges or the preset number of iterations is reached, then the iteration stops and a stable image is generated.

9. A computer device, characterized in that, The method includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the automatic exposure convergence method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the automatic exposure convergence method as described in any one of claims 1 to 7.

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