Image Glare Processing Method and Apparatus, Storage Medium

Abstract

The present application discloses an image floodlight processing method, apparatus, and storage medium, belonging to the technical field of image processing. An electronic device obtains a first brightness region class of a first image, and the first brightness region class includes one or more brightness regions of the first image; after determining that the first brightness region class of the first image is the same as a target brightness region class of a second image that has undergone floodlight processing, the electronic device obtains a first intermediate image obtained by performing Gaussian blur processing on the target brightness region class of the second image; the electronic device generates a floodlight image of the first image based on the first image and the first intermediate image. The present application can reduce the load during the operation of the electronic device after the floodlight processing function is turned on, and reduce the power consumption of the electronic device.

Description

Technical Field

[0001] This application relates to the technical field of image processing, and particularly relates to an image blooming processing method, an apparatus, and a storage medium. Background Art

[0002] Blooming is a common optical phenomenon, generally referring to the phenomenon of halo overflow when a physical camera shoots an object with a relatively high brightness. Performing blooming processing on an image can visually improve the contrast of the image, enhance the expressiveness of the image, and achieve a better rendering effect. With the development of image processing technology, image blooming processing has been widely applied in fields such as three-dimensional game and animation production.

[0003] Currently, the process of an electronic device with the image blooming processing function enabled for performing blooming processing on an image includes: First, perform brightness filtering processing on the original image, remove the pixels with pixel values lower than the brightness threshold in the original image, and obtain a filtered image. Then, perform downsampling processing on the filtered image using reduction ratios of 1 / 4, 1 / 8, 1 / 16, and 1 / 32 respectively, to obtain four low-resolution images with resolutions of 1 / 4 times, 1 / 8 times, 1 / 16 times, and 1 / 32 times that of the original image. Then, perform Gaussian blur processing on the four low-resolution images respectively. Finally, perform image fusion processing on the original image and the four low-resolution images that have undergone Gaussian blur processing to obtain the blooming image of the original image. Among them, the original image includes two parts, a small-sized bright area and a large-sized bright area. Performing Gaussian blur processing on the low-resolution images with resolutions of 1 / 4 times and 1 / 8 times that of the original image is mainly used to achieve blooming processing on the small-sized bright area on the original image, so that the small-sized bright area on the original image achieves a blooming effect; performing Gaussian blur processing on the low-resolution images with resolutions of 1 / 16 times and 1 / 32 times that of the original image is mainly used to achieve blooming processing on the large-sized bright area on the original image, so that the large-sized bright area on the image achieves a blooming effect.

[0004] However, an electronic device with the image blooming processing function enabled will perform the above-mentioned blooming processing process on each frame of the original image respectively. Since one blooming processing process includes multiple Gaussian blur processes performed on bright areas of different sizes in the original image, and the computational complexity of Gaussian blur processing is relatively high, currently, an electronic device with the image blooming processing function enabled has a large load and high power consumption during operation. Summary of the Invention

[0005] This application provides an image blooming processing method, an apparatus, a storage medium, etc., to reduce the load and power consumption of an electronic device with the image blooming processing function enabled during operation.

[0006] The present application will be introduced from different aspects below. It should be understood that the implementation manners and beneficial effects of the following different aspects can be referred to each other.

[0007] In the present application, "first" and "second" are only used to distinguish two objects and do not imply a sequence.

[0008] In a first aspect, an image floodlight processing method is provided. The method includes: an electronic device obtains a first brightness region class of a first image, and the first brightness region class includes one or more brightness regions of the first image. After determining that the first brightness region class of the first image is the same as a target brightness region class of a second image after floodlight processing, the electronic device obtains a first intermediate image obtained by performing Gaussian blur processing on the target brightness region class of the second image. The electronic device generates a floodlight image of the first image based on the first image and the first intermediate image.

[0009] Optionally, the object of floodlight processing can be an image obtained by rendering a three-dimensional scene; it can also be an image directly generated after a physical camera captures a certain scene. In the present application, it is described by taking the first image and the second image as images obtained by rendering a three-dimensional scene as an example. Among them, the first image is obtained by rendering a first three-dimensional scene, and the second image is obtained by rendering a second three-dimensional scene. Optionally, the first image and the second image can be two consecutive frames of images, that is, the second image is the previous frame image of the first image. In this way, the electronic device only needs to store the previous frame image of the currently displayed image, and can complete the judgment operation on the first brightness region class of the first image and the target brightness region class of the second image, and the number of images that the electronic device needs to store is small, which can ensure the storage performance of the electronic device.

[0010] Since in the present application, when the electronic device determines that the first brightness region class of the first image is the same as the target brightness region class of the second image after floodlight processing, it can directly obtain the intermediate image obtained by performing Gaussian blur processing on the target brightness region class of the second image, without performing Gaussian blur processing on the first brightness region class of the first image. Therefore, on the premise of ensuring the floodlight effect of the first image, the present application can reduce the number of times of performing Gaussian blur processing on the first image, thereby reducing the complexity of the image floodlight processing process, and thus reducing the load of the electronic device during operation after turning on the floodlight processing function, and reducing the power consumption of the electronic device.

[0011] Optionally, when the first image is rendered based on the first three-dimensional scene and the second image is rendered based on the second three-dimensional scene, the first brightness region class of the first image is the same as the target brightness region class of the second image, including: the state information of the object model corresponding to the brightness region in the first brightness region class in the first three-dimensional scene is the same as the state information of the object model corresponding to the brightness region in the target brightness region class in the second three-dimensional scene, and the camera parameters of the first three-dimensional scene are the same as the camera parameters of the second three-dimensional scene.

[0012] In some implementation manners, the process by which the electronic device determines whether the first brightness region class of the first image is the same as the target brightness region class of the second image may include:

[0013] The electronic device obtains the state information of all object models corresponding to the first brightness region class in the first three-dimensional scene, and the state information of all object models corresponding to the target brightness region class in the second three-dimensional scene. After determining that the state information of all object models corresponding to the first brightness region class in the first three-dimensional scene is the same as the state information of all object models corresponding to the target brightness region class in the second three-dimensional scene, the electronic device obtains the camera parameters of the first three-dimensional scene and the camera parameters of the second three-dimensional scene. After determining that the camera parameters of the first three-dimensional scene are the same as the camera parameters of the second three-dimensional scene, the electronic device determines that the first brightness region class is the same as the target brightness region class.

[0014] Optionally, the state information of the object model may include pose information and surface material information. Among them, the pose information may include the position of the object model, the pose of the object model, and the scaling factor of the object model. The surface material information may include: the color information of the surface material of the object model and the texture information of the surface material. The camera parameters include: the pose parameters of the camera, the window parameters, and the field of view parameters.

[0015] The state information of all object models corresponding to the first brightness region class in the first 3D scene being the same as the state information of all object models corresponding to the target brightness region class in the second 3D scene means that the first brightness regions in the first brightness region class and the second brightness regions in the target brightness region class correspond one by one, and the state information of the object model corresponding to each first brightness region is the same as the state information of the object model corresponding to the corresponding second brightness region.

[0016] In this application, when the first brightness region class of the first image is the same as the target brightness region class of the second image after floodlight processing, the electronic device can directly obtain the first intermediate image obtained by performing Gaussian blur processing on the target brightness region, and use the first intermediate image as the image obtained by performing Gaussian blur processing on the first brightness region class of the first image. At this time, during the operation of the electronic device with the image floodlight processing function enabled, there is no need to perform Gaussian blur processing on the first brightness region class of the first image, reducing the operating load of the electronic device and its power consumption.

[0017] Optionally, the first image further includes a second brightness region, and the second brightness region class includes other brightness regions in the first image except for the background brightness region.

[0018] In some implementation manners, after the electronic device determines that the second brightness region class of the first image is different from any brightness region class of the second image, it can also perform Gaussian blur processing on the second brightness region class to obtain a second intermediate image. Then, the process by which the electronic device generates the floodlight image of the first image based on the first image and the first intermediate image includes: the electronic device performs image fusion processing on the first image, the first intermediate image, and the second intermediate image to obtain the floodlight image of the first image.

[0019] Among them, the electronic device can first determine whether there is a certain brightness region class in the second image after floodlight processing that is the same as the second brightness region class of the first image. After determining that the second brightness region class of the first image is different from any brightness region class of the second image, it performs Gaussian blur processing on the second brightness region class to obtain a second intermediate image. Among them, the process by which the electronic device can first determine whether there is a certain brightness region class in the second image after floodlight processing that is the same as the second brightness region class of the first image can refer to the above process by which the electronic device determines whether the first brightness region class of the first image is the same as the target brightness region class of the second image after floodlight processing, and this application will not elaborate on this.

[0020] Optionally, when the sizes of the brightness regions in the first brightness region class are all larger than those in the second brightness region class, the process of the electronic device performing Gaussian blur processing on the second brightness region class of the first image includes: performing downsampling processing on the first image with a first downsampling ratio to obtain a first downsampled image. Performing Gaussian blur processing on the first downsampled image to obtain a second intermediate image. Or, when the sizes of the brightness regions in the first brightness region class are all smaller than those in the second brightness region class, the process of the electronic device performing Gaussian blur processing on the second brightness region class of the first image includes: performing downsampling processing on the first image with a second downsampling ratio to obtain a second downsampled image. Performing Gaussian blur processing on the second downsampled image to obtain a second intermediate image. Wherein, the first downsampling ratio is greater than the second downsampling ratio.

[0021] Optionally, the first downsampling ratio k1 satisfies: k1 = 2 m , m is an integer, and -3 ≤ n ≤ 0. In this application, the first downsampling ratio can refer to a single downsampling ratio or a set of multiple downsampling ratios. For example, if n can take values -2 or -3, then the first downsampling ratio k1 includes 1 / 4 and 1 / 8. The second downsampling ratio k2 can satisfy: k2 = 2 m , m is an integer, and m < -3. In this application, the second downsampling ratio can refer to a single downsampling ratio or a set of multiple downsampling ratios. For example, if m can take values -4 or -5, then the second downsampling ratio k2 includes 1 / 16 and 1 / 32.

[0022] It can be seen that when the first image includes multiple brightness region classes, the electronic device can also directly obtain the intermediate image obtained by performing Gaussian blur processing on the brightness region class in the second image that is the same as the second brightness region class of the first image when it is determined that there is a brightness region class in the second image after floodlight processing that is the same as the second brightness region class of the first image, without performing Gaussian blur processing on the second brightness region class of the first image. Therefore, this application can further reduce the number of times of performing Gaussian blur processing on the first image on the premise of ensuring the floodlight effect of the first image, thereby reducing the complexity of the image floodlight processing process, reducing the load of the electronic device during operation after turning on the floodlight processing function, and reducing the power consumption of the electronic device.

[0023] In some implementation manners, the electronic device can also perform Gaussian blur processing on the second brightness region class to obtain a second intermediate image. Then the process of the electronic device generating the floodlight image of the first image based on the first image and the first intermediate image includes: the electronic device performing image fusion processing on the first image, the first intermediate image, and the second intermediate image to obtain the floodlight image of the first image.

[0024] Optionally, the process by which the electronic device obtains the first luminance region class of the first image may include: traversing the labels of all object models in the first three-dimensional scene, where the label is used to indicate whether the object model is a background class object model. Obtaining all background class object models in the first three-dimensional scene, and the first luminance region class includes the background class luminance regions corresponding to all background class object models in the first image.

[0025] Optionally, before generating the omnidirectional illumination image of the first image based on the first image and the first intermediate image, the electronic device may further perform luminance filtering processing on the first image.

[0026] Among them, when the electronic device performs luminance filtering processing on the first image, that is, removing the pixels with pixel values less than the luminance threshold in the first image, so as to retain the pixels with pixel values greater than or equal to the luminance threshold in the first image.

[0027] In a second aspect, an omnidirectional illumination image processing apparatus is provided. The apparatus includes a plurality of functional modules, and these functional modules interact with each other to implement the methods in the first aspect and its various implementation manners above. The plurality of functional modules may be implemented based on software, hardware, or a combination of software and hardware, and the plurality of functional modules may be arbitrarily combined or divided based on specific implementations.

[0028] In a third aspect, an omnidirectional illumination image processing apparatus, such as a terminal, is provided. The omnidirectional illumination image processing apparatus includes a processor and a memory. The processor generally includes a CPU and a GPU. The memory is used to store a computer program; the CPU is used to implement any one of the omnidirectional illumination image processing methods in the first aspect when executing the computer program stored in the memory. These two types of processors may be two chips or integrated on the same chip.

[0029] In a fourth aspect, a storage medium is provided, and the storage medium may be non-volatile. The storage medium stores a computer program, and when the computer program is executed by a processing component, the processing component is caused to implement any one of the omnidirectional illumination image processing methods in the first aspect.

[0030] In a fifth aspect, a computer program or a computer program product including computer-readable instructions is provided. When the computer program or the computer program product runs on a computer, the computer is caused to execute any one of the omnidirectional illumination image processing methods in the first aspect. The computer program product may include one or more program units for implementing any one of the omnidirectional illumination image processing methods in the first aspect.

[0031] In a sixth aspect, a chip, such as a CPU, is provided. The chip includes a logic circuit, and the logic circuit may be a programmable logic circuit. When the chip runs, it is used to implement any one of the omnidirectional illumination image processing methods in the first aspect.

[0032] In a seventh aspect, a chip, such as a CPU, is provided. The chip includes one or more physical cores and a storage medium. After reading computer instructions in the storage medium, the one or more physical cores implement any one of the foregoing image floodlight processing methods in the first aspect.

[0033] In summary, for the image floodlight processing method provided in this application, since the electronic device can directly obtain the intermediate image obtained by performing Gaussian blur processing on the target brightness region class of the second image when determining that the first brightness region class in the first image is the same as the target brightness region class of the second image after floodlight processing, without performing Gaussian blur processing on the first brightness region class of the first image. Therefore, on the premise of ensuring the floodlight effect of the first image, this application can reduce the number of times of performing Gaussian blur processing on the first image, thereby reducing the complexity of the image floodlight processing process, reducing the load during the operation of the electronic device after turning on the floodlight processing function, and reducing the power consumption of the electronic device.

[0034] In addition, this application also has the effects mentioned in the foregoing aspects and other derivable technical effects, which will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a schematic diagram of floodlight in a shooting scene provided by an embodiment of this application;

[0036] Figure 2 is a schematic structural diagram of an electronic device involved in an image floodlight processing method provided by an embodiment of this application;

[0037] Figure 3 is a schematic diagram of the form of an electronic device provided by an embodiment of this application in combination with hardware and software;

[0038] Figure 4 is a schematic flowchart of a process for creating a 3D scene provided by an embodiment of this application;

[0039] Figure 5 is a schematic flowchart of a process for rendering an image from a 3D scene created according to a graphics application provided by an embodiment of this application;

[0040] Figure 6 is a flowchart of an image floodlight processing method provided by an embodiment of this application;

[0041] Figure 7 is a flowchart of a method for determining whether the first brightness region class of the first image is the same as the target brightness region class of the second image provided by an embodiment of this application;

[0042] Figure 8 is a flowchart of another image floodlight processing method provided by an embodiment of this application;

[0043] Figure 9 It is a schematic diagram of a 3D game interface provided by an embodiment of the present application;

[0044] Figure 10 It is a flowchart of another method for image bloom processing provided by an embodiment of the present application;

[0045] Figure 11 It is a schematic flowchart of a method for an electronic device to implement image bloom processing provided by an embodiment of the present application;

[0046] Figure 12 It is a block diagram of an image bloom processing device provided by an embodiment of the present application;

[0047] Figure 13 It is a block diagram of another image bloom processing device provided by an embodiment of the present application;

[0048] Figure 14 It is a block diagram of yet another image bloom processing device provided by an embodiment of the present application;

[0049] Figure 15 It is a block diagram of still another image bloom processing device provided by an embodiment of the present application;

[0050] Figure 16 It is a schematic structural diagram of yet another image bloom processing device provided by an embodiment of the present application. Detailed implementation manners

[0051] To make the objectives, technical solutions and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0052] With the development of computer graphics hardware, the requirements for the image presentation effects in fields such as games and movies are getting higher and higher. The image presentation effects in games (such as three-dimensional (3D) games) and movies (such as animations) produced with 3A-level specifications are getting closer and closer to the shooting effects of physical cameras. Currently, the image presentation effects can be made closer to those of physical cameras by performing post-processing on the images.

[0053] For ease of understanding, the following explains the terms involved in the embodiments of the present application.

[0054] Image post - processing: It refers to the process of optimizing images, mainly used to enhance features such as image anti - aliasing, high dynamic range (HDR) images, and bloom. Image post - processing techniques include image - processing techniques such as bloom processing, anti - aliasing processing, motion blur processing, and depth - of - field processing. To a certain extent, image post - processing techniques can be considered similar to the filter processing techniques in PS (Photoshop). The object of image post - processing can be an image rendered based on a 3D scene.

[0055] Bloom: It is a common optical phenomenon. Since a physical camera (i.e., a real camera or camcorder) usually cannot focus perfectly when taking a picture, during the process of light passing through the lens of the physical camera to form an image, diffraction occurs at the edges of objects, resulting in a phenomenon of halo overflow. Bloom is not easily noticeable in scenes with low brightness (weak light), but it is more obvious in scenes with high brightness (strong light). Therefore, bloom generally refers to the halo overflow phenomenon that occurs when a physical camera takes pictures of objects with high brightness. For example, Figure 1 is a schematic diagram of bloom in a shooting scene provided by an embodiment of this application. As Figure 1 shown, there is a bright window in the shooting scene. For the indoor scene, since the object brightness is low, the indoor objects photographed by the physical camera have clear outlines. For the outdoor scene, since the sun outside the window has high brightness, the light emitted by the sun will exceed the outline of the sun itself, producing a blurred effect around its own outline, that is, the bloom phenomenon appears.

[0056] Bloom effect: In computer graphics, the bloom effect, also known as specular highlight, is a computer graphics effect used in video games, demonstration animations, and HDR. The bloom effect will produce stripes or feather - like glows around high - brightness objects to blur image details, that is, to imitate the bloom phenomenon in the physical camera imaging process, making the images rendered by electronic devices appear more realistic.

[0057] By performing bloom processing on an image, the image can have a bloom effect, thereby visually improving the contrast of the image, enhancing the expressiveness and authenticity of the image, and achieving a better rendering effect. Optionally, the object of bloom processing can be an image rendered based on a 3D scene; it can also be an image directly generated after a physical camera shoots a certain scene. In the embodiments of this application, the object of bloom processing is taken as an image rendered based on a 3D scene as an example for illustration.

[0058] The process of the electronic device with the image bloom processing function enabled for performing bloom processing on an image includes: First, perform brightness filtering on the original image, removing the pixels with pixel values lower than the brightness threshold in the original image to obtain a filtered image. Then, perform downsampling pixel sampling on the filtered image with reduction ratios of 1 / 4, 1 / 8, 1 / 16, and 1 / 32 respectively, to obtain four low-resolution images with resolutions of 1 / 4 times, 1 / 8 times, 1 / 16 times, and 1 / 32 times of the original image. Then, perform Gaussian blur processing on the four low-resolution images respectively. Finally, perform image fusion processing on the original image and the four low-resolution images after Gaussian blur processing to obtain the bloom image of the original image. However, the electronic device with the image bloom processing function enabled performs the above bloom processing process on each frame of the original image respectively. Since one bloom processing process includes multiple Gaussian blur processes performed on brightness regions of different sizes in the original image, and the computational complexity of the Gaussian blur process is relatively high, the electronic device with the image bloom processing function enabled currently has a large load and high power consumption during operation.

[0059] The embodiment of the present application provides an image bloom processing method. When the electronic device with the image bloom processing function enabled performs bloom processing on the first image, it can obtain the first brightness region class of the first image. When the first brightness region class of the first image is the same as the target brightness region class of the second image that has undergone bloom processing, it obtains the first intermediate image obtained after performing Gaussian blur processing on the target brightness region class of the second image, and generates the bloom image of the first image based on the first image and the first intermediate image. Since in the embodiment of the present application, when the electronic device determines that the first brightness region class of the first image is the same as the target brightness region class of the second image that has undergone bloom processing, it can directly obtain the intermediate image obtained after performing Gaussian blur processing on the target brightness region class of the second image, without performing Gaussian blur processing on the first brightness region class of the first image. Therefore, the embodiment of the present application can reduce the number of times of performing Gaussian blur processing on the first image on the premise of ensuring the bloom effect of the first image, thereby reducing the complexity of the image bloom processing process, and thus reducing the load of the electronic device during operation after enabling the bloom processing function and reducing the power consumption of the electronic device.

[0060] The embodiments of this application are described by taking the first image and the second image as images rendered based on a 3D scene as an example. Among them, the first image is rendered based on a first 3D scene, and the second image is rendered based on a second 3D scene. The second image is the image displayed by the electronic device before displaying the first image. Optionally, the first image and the second image can be two consecutive frames of images, that is, the second image is the previous frame of the first image. In this way, the electronic device only needs to store the previous frame of the image currently being displayed, and can complete the judgment operation on the first brightness region class of the first image and the target brightness region class of the second image. Moreover, the number of images that the electronic device needs to store is small, which can ensure the storage performance of the electronic device.

[0061] Figure 2 FIG. 4 is a schematic structural diagram of an electronic device 200 involved in an image floodlight processing method provided by an embodiment of this application. The electronic device 200 can be, but is not limited to, a laptop computer, a desktop computer, a mobile phone, a smartphone, a tablet computer, a multimedia player, an e-reader, an intelligent vehicle-mounted device, an intelligent home appliance, an artificial intelligence device, a wearable device, an Internet of Things device, or a virtual reality / augmented reality / mixed reality device, etc.

[0062] The electronic device 200 may include a processor 210, an external memory interface 220, an internal memory 221, a universal serial bus (USB) interface 230, a charging management module 240, a power management module 241, a battery 242, an antenna 1, an antenna 2, a mobile communication module 250, a wireless communication module 260, an audio module 270, a speaker 270A, a receiver 270B, a microphone 270C, a headphone interface 270D, a sensor module 280, a button 290, a motor 291, an indicator 292, a camera 293, a display screen 294, and a subscriber identification module (SIM) card interface 295, etc. Among them, the sensor module 280 may include a pressure sensor 280A, a gyroscope sensor 280B, a barometric pressure sensor 280C, a magnetic sensor 280D, an acceleration sensor 280E, a distance sensor 280F, a proximity light sensor 280G, a fingerprint sensor 280H, a temperature sensor 280J, a touch sensor 280K, an ambient light sensor 280L, a bone conduction sensor 280M, etc.

[0063] It can be understood that the structure schematically shown in the embodiments of this application does not constitute a specific limitation on the electronic device 200. In other embodiments of this application, the electronic device 200 may include more or fewer components than those shown in the figure, or combine certain components, or split certain components, or have different component arrangements. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0064] It should be understood that the interface connection relationships among the modules illustrated in the embodiments of the present application are only illustrative descriptions and do not constitute a structural limitation on the electronic device 200. In other embodiments of the present application, the electronic device 200 may also adopt different interface connection methods (such as a bus connection method) in the above embodiments, or a combination of multiple interface connection methods.

[0065] The processor 210 may include one or more processing units. For example, it includes a central processing unit (CPU) (such as an application processor (AP)), a graphics processing unit (GPU). Further, it may also include a modem processor, an image signal processor (ISP), a microcontroller unit (MCU), a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0066] A memory may also be provided in the processor 210 for storing instructions and data. In some embodiments, the memory in the processor 210 is a cache memory. This memory can save the instructions or data that the processor 210 has just used or recycled. If the processor 210 needs to use the instruction or data again, it can directly call it from the memory. This avoids repeated accesses, reduces the waiting time of the processor 210, and thus improves the efficiency of the system.

[0067] In some embodiments, the processor 210 may include one or more interfaces. The interfaces may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0068] The I2C interface is a two-way synchronous serial bus, including a serial data line (SDA) and a serial clock line (SCL). In some embodiments, the processor 210 may include multiple groups of I2C buses. The processor 210 may be respectively coupled to the touch sensor 280K, the charger, the flashlight, the camera 293, etc. through different I2C bus interfaces. For example, the processor 210 may be coupled to the touch sensor 280K through the I2C interface, enabling the processor 210 to communicate with the touch sensor 280K through the I2C bus interface to implement the touch function of the electronic device 200.

[0069] The I2S interface can be used for audio communication. In some embodiments, the processor 210 may include multiple groups of I2S buses. The processor 210 may be coupled to the audio module 270 through the I2S bus to implement communication between the processor 210 and the audio module 270. In some embodiments, the audio module 270 may transmit an audio signal to the wireless communication module 260 through the I2S interface to implement the function of answering a call through a Bluetooth headset.

[0070] The PCM interface can also be used for audio communication to sample, quantize, and encode analog signals. In some embodiments, the audio module 270 and the wireless communication module 260 may be coupled through the PCM bus interface. In some embodiments, the audio module 270 may also transmit an audio signal to the wireless communication module 260 through the PCM interface to implement the function of answering a call through a Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.

[0071] The UART interface is a general-purpose serial data bus for asynchronous communication. This bus can be a bidirectional communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, the UART interface is typically used to connect the processor 210 and the wireless communication module 260. For example, the processor 210 communicates with the Bluetooth module in the wireless communication module 260 through the UART interface to implement the Bluetooth function. In some embodiments, the audio module 270 can transmit audio signals to the wireless communication module 260 through the UART interface to implement the function of playing music through Bluetooth headsets.

[0072] The MIPI interface can be used to connect the processor 210 with peripheral devices such as the display screen 294 and the camera 293. The MIPI interface includes a camera serial interface (CSI), a display serial interface (DSI), etc. In some embodiments, the processor 210 and the camera 293 communicate through the CSI interface to implement the shooting function of the electronic device 200. The processor 210 and the display screen 294 communicate through the DSI interface to implement the display function of the electronic device 200.

[0073] The GPIO interface can be configured by software. The GPIO interface can be configured as a control signal or a data signal. In some embodiments, the GPIO interface can be used to connect the processor 210 with the camera 293, the display screen 294, the wireless communication module 260, the audio module 270, the sensor module 280, etc. The GPIO interface can also be configured as an I2C interface, an I2S interface, a UART interface, a MIPI interface, etc.

[0074] The USB interface 230 is an interface that complies with the USB standard specification, and can specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, etc. The USB interface 230 can be used to connect a charger to charge the electronic device 200, and can also be used to transfer data between the electronic device 200 and peripheral devices. It can also be used to connect headphones to play audio through the headphones. This interface can also be used to connect other electronic devices, such as AR devices, etc.

[0075] The charging management module 240 is used to receive a charging input from a charger. The charger can be a wireless charger or a wired charger. In some embodiments of wired charging, the charging management module 240 can receive the charging input from a wired charger through the USB interface 230. In some embodiments of wireless charging, the charging management module 240 can receive a wireless charging input through the wireless charging coil of the electronic device 200. While charging the battery 242, the charging management module 240 can also supply power to the electronic device through the power management module 241.

[0076] The power management module 241 is used to connect the battery 242, the charging management module 240, and the processor 210. The power management module 241 receives inputs from the battery 242 and / or the charging management module 240 and supplies power to the processor 210, the internal memory 221, the display screen 294, the camera 293, the wireless communication module 260, etc. The power management module 241 can also be used to monitor parameters such as the battery capacity, the number of battery charge cycles, and the battery health status (leakage, impedance). In some other embodiments, the power management module 241 can also be disposed in the processor 210. In some other embodiments, the power management module 241 and the charging management module 240 can also be disposed in the same device.

[0077] The wireless communication function of the electronic device 200 can be implemented through Antenna 1, Antenna 2, the mobile communication module 250, the wireless communication module 260, the modulation and demodulation processor, and the baseband processor, etc.

[0078] Antenna 1 and Antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the electronic device 200 can be used to cover a single or multiple communication frequency bands. Different antennas can also be multiplexed to improve the utilization rate of the antennas. For example, Antenna 1 can be multiplexed as a diversity antenna for a wireless local area network. In some other embodiments, the antenna can be used in combination with a tuning switch.

[0079] The mobile communication module 250 may provide a solution for wireless communication including 2G / 3G / 4G / 5G, etc., which is applied to the electronic device 200. The mobile communication module 250 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The mobile communication module 250 may receive electromagnetic waves through the antenna 1, filter, amplify, etc. the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 250 may also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves through the antenna 1 and radiate them out. In some embodiments, at least some functional modules of the mobile communication module 250 may be disposed in the processor 210. In some embodiments, at least some functional modules of the mobile communication module 250 and at least some modules of the processor 210 may be disposed in the same device.

[0080] The modulation and demodulation processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. Subsequently, the demodulator transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After being processed by the baseband processor, the low-frequency baseband signal is transmitted to the application processor. The application processor outputs a sound signal through an audio device (not limited to the speaker 270A, receiver 270B, etc.), or displays an image or video through the display screen 294. In some embodiments, the modulation and demodulation processor may be an independent device. In other embodiments, the modulation and demodulation processor may be independent of the processor 210 and disposed in the same device as the mobile communication module 250 or other functional modules.

[0081] The wireless communication module 260 may provide solutions for wireless communications applied to the electronic device 200, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite systems (GNSSs), frequency modulation (FM), near field communication (NFC), infrared (IR), etc. The wireless communication module 260 may be one or more devices integrating at least one communication processing module. The wireless communication module 260 receives electromagnetic waves via the antenna 2, performs frequency modulation and filtering processing on the electromagnetic wave signals, and sends the processed signals to the processor 210. The wireless communication module 260 may also receive signals to be sent from the processor 210, perform frequency modulation and amplification on them, and convert them into electromagnetic waves through the antenna 2 for radiation.

[0082] In some embodiments, antenna 1 of electronic device 200 is coupled to mobile communication module 250, and antenna 2 is coupled to wireless communication module 260, such that electronic device 200 can communicate with a network and other devices through wireless communication technologies. The wireless communication technologies may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technology, etc. The GNSS may include global positioning system (GPS), global navigation satellite system (GLONASS), beidou navigation satellite system (BDS), quasi-zenith satellite system (QZSS), and / or satellite based augmentation systems (SBAS).

[0083] Electronic device 200 implements a display function through a GPU, display screen 294, and an application processor, etc. The GPU is a microprocessor for image processing, and is connected to display screen 294 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 210 may include one or more GPUs, which execute program instructions to generate or change display information.

[0084] The display screen 294 is used to display images, videos, etc. The display screen 294 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a Mini LED, a Micro-LED, a Micro-OLED, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 200 may include one or N display screens 294, where N is a positive integer greater than 1.

[0085] The electronic device 200 can implement the shooting function through the ISP, the camera 293, the video codec, the GPU, the display screen 294, and the application processor, etc.

[0086] The ISP is used to process the data fed back by the camera 293. For example, when taking a photo, the shutter is opened, and the light passes through the lens and is transmitted to the camera photosensitive element. The optical signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to the ISP for processing and converts it into an image visible to the naked eye. The ISP can also optimize the noise, brightness, and skin color of the image through algorithms. The ISP can also optimize parameters such as the exposure and color temperature of the shooting scene. In some embodiments, the ISP can be provided in the camera 293.

[0087] The camera 293 is used to capture static images or videos. An object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then transmits the electrical signal to the ISP to convert it into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in standard RGB, YUV, etc. formats. In some embodiments, the electronic device 200 may include one or N cameras 293, where N is a positive integer greater than 1.

[0088] The digital signal processor is used to process digital signals. In addition to being able to process digital image signals, it can also process other digital signals. For example, when the electronic device 200 selects a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy, etc.

[0089] The video codec is used to compress or decompress digital videos. The electronic device 200 can support one or more video codecs. In this way, the electronic device 200 can play or record videos in multiple encoding formats, such as: Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

[0090] The NPU is a neural-network (NN) computing processor. By drawing on the structure of biological neural networks, such as the transmission pattern between human brain neurons, it can quickly process input information and can also continuously learn on its own. Through the NPU, applications such as intelligent cognition of the electronic device 200 can be realized, such as: image recognition, face recognition, speech recognition, text understanding, etc.

[0091] The external memory interface 220 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 200. The external memory card communicates with the processor 210 through the external memory interface 220 to implement the data storage function. For example, files such as music and videos are saved in the external memory card.

[0092] The internal memory 221 can be used to store computer-executable program code, and the executable program code includes instructions. The internal memory 221 can include a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function (such as the sound playback function, the image playback function, etc.). The data storage area can store data created during the use of the electronic device 200 (such as audio data, phone book, etc.). In addition, the internal memory 221 can include high-speed random access memory, such as double data rate synchronous dynamic random access memory (DDR), and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc. The processor 210 executes various functional applications and data processing of the electronic device 200 by running the instructions stored in the internal memory 221 and / or the instructions stored in the memory provided in the processor.

[0093] The electronic device 200 can implement audio functions through the audio module 270, speaker 270A, receiver 270B, microphone 270C, headphone jack 270D, and application processor, etc. For example, music playback, recording, etc.

[0094] The audio module 270 is used to convert digital audio information into an analog audio signal for output, and is also used to convert analog audio input into a digital audio signal. The audio module 270 can also be used for encoding and decoding audio signals. In some embodiments, the audio module 270 can be disposed in the processor 210, or some functional modules of the audio module 270 can be disposed in the processor 210.

[0095] The speaker 270A, also known as the "loudspeaker", is used to convert an audio electrical signal into a sound signal. The electronic device 200 can listen to music or hands-free calls through the speaker 270A.

[0096] The receiver 270B, also known as the "earpiece", is used to convert an audio electrical signal into a sound signal. When the electronic device 200 answers a call or a voice message, the voice can be listened to by placing the receiver 270B close to the human ear.

[0097] The microphone 270C, also known as the "microphone" or "transmitter", is used to convert a sound signal into an electrical signal. When making a call or sending a voice message, the user can speak by bringing the mouth close to the microphone 270C to input the sound signal into the microphone 270C. The electronic device 200 can be provided with at least one microphone 270C. In some other embodiments, the electronic device 200 can be provided with two microphones 270C, which can not only collect sound signals but also implement a noise reduction function. In some other embodiments, the electronic device 200 can also be provided with three, four or more microphones 270C to collect sound signals, reduce noise, identify the sound source, and implement functions such as directional recording.

[0098] The headphone jack 270D is used to connect a wired headphone. The headphone jack 270D can be a USB interface 230, or a 3.5 mm open mobile terminal platform (OMTP) standard interface, or a cellular telecommunications industry association of the USA (CTIA) standard interface.

[0099] The pressure sensor 280A is used to sense pressure signals and can convert pressure signals into electrical signals. In some embodiments, the pressure sensor 280A may be disposed on the display screen 294. There are many types of pressure sensors 280A, such as resistive pressure sensors, inductive pressure sensors, capacitive pressure sensors, etc. The capacitive pressure sensor may include at least two parallel plates having conductive materials. When a force acts on the pressure sensor 280A, the capacitance between the electrodes changes. The electronic device 200 determines the intensity of the pressure based on the change in capacitance. When a touch operation acts on the display screen 294, the electronic device 200 detects the intensity of the touch operation according to the pressure sensor 280A. The electronic device 200 can also calculate the position of the touch based on the detection signal of the pressure sensor 280A. In some embodiments, touch operations acting on the same touch position but with different touch operation intensities may correspond to different operation instructions. For example: when a touch operation with a touch operation intensity less than the first pressure threshold acts on the short message application icon, the instruction to view the short message is executed. When a touch operation with a touch operation intensity greater than or equal to the first pressure threshold acts on the short message application icon, the instruction to create a new short message is executed.

[0100] The gyroscope sensor 280B can be used to determine the motion posture of the electronic device 200. In some embodiments, the angular velocity of the electronic device 200 around three axes (i.e., the x, y, and z axes) can be determined by the gyroscope sensor 280B. The gyroscope sensor 280B can be used for anti-shake during shooting. Exemplarily, when the shutter is pressed, the gyroscope sensor 280B detects the angle of jitter of the electronic device 200, calculates the distance that the lens module needs to compensate based on the angle, and makes the lens offset the jitter of the electronic device 200 through reverse movement to achieve anti-shake. The gyroscope sensor 280B can also be used for navigation and somatosensory game scenarios.

[0101] The barometric pressure sensor 280C is used to measure barometric pressure. In some embodiments, the electronic device 200 calculates the altitude based on the barometric pressure value measured by the barometric pressure sensor 280C to assist in positioning and navigation.

[0102] The magnetic sensor 280D includes a Hall sensor. The electronic device 200 can use the magnetic sensor 22280D to detect the opening and closing of the flip leather case. In some embodiments, when the electronic device 200 is a flip phone, the electronic device 200 can detect the opening and closing of the flip according to the magnetic sensor 280D. Furthermore, according to the detected opening and closing state of the leather case or the opening and closing state of the flip, features such as automatic flip unlocking are set.

[0103] The acceleration sensor 280E can detect the magnitude of the acceleration of the electronic device 200 in various directions (generally three axes). When the electronic device 200 is stationary, the magnitude and direction of gravity can be detected. It can also be used to identify the posture of the electronic device and is applied to applications such as horizontal and vertical screen switching and pedometers.

[0104] A distance sensor 280F for measuring distance. The electronic device 200 can measure the distance through infrared or laser. In some embodiments, when shooting a scene, the electronic device 200 can use the distance sensor 280F to measure the distance to achieve fast focusing.

[0105] The proximity light sensor 280G may include, for example, a light-emitting diode (LED) and a light detector, such as a photodiode. The light-emitting diode may be an infrared light-emitting diode. The electronic device 200 emits infrared light outward through the light-emitting diode. The electronic device 200 uses the photodiode to detect the infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the electronic device 200. When insufficient reflected light is detected, the electronic device 200 can determine that there is no object near the electronic device 200. The electronic device 200 can use the proximity light sensor 280G to detect that the user holds the electronic device 200 close to the ear for a call, so as to automatically turn off the screen to save power. The proximity light sensor 280G can also be used for automatic unlocking and locking of the holster mode and pocket mode.

[0106] The ambient light sensor 280L is used to sense the ambient light brightness. The electronic device 200 can adaptively adjust the brightness of the display screen 294 according to the sensed ambient light brightness. The ambient light sensor 280L can also be used to automatically adjust the white balance when taking pictures. The ambient light sensor 280L can also cooperate with the proximity light sensor 280G to detect whether the electronic device 200 is in the pocket to prevent accidental touch.

[0107] The fingerprint sensor 280H is used to collect fingerprints. The electronic device 200 can use the collected fingerprint characteristics to achieve fingerprint unlocking, access application locks, fingerprint photography, fingerprint answering calls, etc.

[0108] The temperature sensor 280J is used to detect temperature. In some embodiments, the electronic device 200 uses the temperature detected by the temperature sensor 280J to execute a temperature processing strategy. For example, when the temperature reported by the temperature sensor 280J exceeds a threshold, the electronic device 200 reduces the performance of the processor located near the temperature sensor 280J to reduce power consumption and implement thermal protection. In other embodiments, when the temperature is lower than another threshold, the electronic device 200 heats the battery 242 to avoid abnormal shutdown of the electronic device 200 caused by low temperature. In still other embodiments, when the temperature is lower than yet another threshold, the electronic device 200 boosts the output voltage of the battery 242 to avoid abnormal shutdown caused by low temperature.

[0109] The touch sensor 280K, also known as the "touch control device". The touch sensor 280K can be disposed on the display screen 294, and the touch sensor 280K and the display screen 294 form a touch screen, also known as the "touch control screen". The touch sensor 280K is used to detect touch operations acting thereon or nearby. The touch sensor can transmit the detected touch operation to the application processor to determine the type of touch event. Visual output related to the touch operation can be provided through the display screen 294. In some other embodiments, the touch sensor 280K can also be disposed on the surface of the electronic device 200, at a different position from that of the display screen 294.

[0110] The bone conduction sensor 280M can acquire vibration signals. In some embodiments, the bone conduction sensor 280M can acquire vibration signals of the vibrating bone mass of the human vocal part. The bone conduction sensor 280M can also contact the human pulse to receive blood pressure pulsation signals. In some embodiments, the bone conduction sensor 280M can also be disposed in the earphone to form a bone conduction earphone. The audio module 270 can parse out voice signals based on the vibration signals of the vibrating bone mass of the human vocal part acquired by the bone conduction sensor 280M to implement the voice function. The application processor can parse out heart rate information based on the blood pressure pulsation signals acquired by the bone conduction sensor 280M to implement the heart rate detection function.

[0111] In some other embodiments of the present application, the electronic device 200 can also adopt different interface connection methods in the above embodiments. For example, some or all of the above multiple sensors are connected to the MCU, and then connected to the AP through the MCU.

[0112] The button 290 includes a power-on button, a volume button, etc. The button 290 can be a mechanical button or a touch button. The electronic device 200 can receive button inputs to generate key signal inputs related to the user settings and function controls of the electronic device 200.

[0113] The motor 291 can generate vibration prompts. The motor 291 can be used for incoming call vibration prompts and can also be used for touch vibration feedback. For example, touch operations acting on different applications (such as taking pictures, audio playing, etc.) can correspond to different vibration feedback effects. For touch operations acting on different regions of the display screen 294, the motor 291 can also correspond to different vibration feedback effects. Different application scenarios (such as time reminder, receiving information, alarm clock, game, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

[0114] The indicator 292 can be an indicator light, which can be used to indicate the charging state, power change, and can also be used to indicate messages, missed calls, notifications, etc.

[0115] The SIM card interface 295 is used to connect to a SIM card. The SIM card can be inserted into or removed from the SIM card interface 295 to achieve contact and separation from the electronic device 200. The electronic device 200 may support 2 or N SIM card interfaces, where N is a positive integer greater than 2. The SIM card interface 295 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. Multiple cards can be inserted into the same SIM card interface 295 simultaneously. The types of the multiple cards can be the same or different. The SIM card interface 295 can also be compatible with different types of SIM cards. The SIM card interface 295 can also be compatible with external memory cards. The electronic device 200 interacts with the network through the SIM card to achieve functions such as calls and data communication. In some embodiments, the electronic device 200 uses an eSIM, i.e., an embedded SIM card. The eSIM card can be embedded in the electronic device 200 and cannot be separated from the electronic device 200.

[0116] The software system of the electronic device 200 can adopt a layered architecture, an event-driven architecture, a microkernel architecture, a microservices architecture, or a cloud architecture. In the embodiments of this application, the Android system with a layered architecture is taken as an example to exemplarily illustrate the software structure of the electronic device 200. Of course, the operating system of the electronic device 200 can also be other systems such as the IOS system, and the embodiments of this application do not limit this.

[0117] Exemplarily, Figure 3 is a schematic diagram of the form of an electronic device provided by the embodiments of this application in combination with hardware and software. As Figure 3 shown, the electronic device 200 includes an Android system, software, and hardware. The hardware includes a processor 304, such as a GPU and a CPU, etc. The software includes one or more graphics applications. For example, referring to Figure 3 , the software includes a graphics application 301A and a graphics application 301B (collectively referred to as the graphics application 301). The software also includes a rendering engine module 302 and a graphics application programming interface (API) layer 303. Exemplarily, the graphics applications 301 in the software can include game applications and 3D drawing applications, etc. Figure 3 The number of graphics applications in

[0118] is only used for exemplary illustration and does not limit the electronic device provided by the embodiments of this application. Figure 3, the rendering engine module 302 includes a scene management module 3021, a renderer module 3022, and a post-processing bloom effect module 3023. The scene management module 3021 includes a brightness area class change judgment module 30211, and the scene management module 3021 has a rendering engine interface; the post-processing bloom effect module 3023 includes an optimized large-dimension highlight area bloom algorithm module 30231. The graphics API layer 303 includes an OpenGL for embedded systems (OpenGL ES) interface layer 3031 and a Vulkan interface (a cross-platform graphics application programming interface) layer 3032. OpenGL ES is a graphics library designed for embedded devices such as mobile phones, personal digital assistants (PDAs), and game consoles in an open system.

[0119] As Figure 4 shown, the graphics application creates a 3D scene by loading one or more object models.

[0120] The graphics application 301 loads one or more object models, that is, obtains the relevant data of the one or more object models. The relevant data of each object model includes the state information of the object model. The graphics application 301 creates a 3D scene (also called a rendering scene) according to the state information of the one or more object models. Optionally, the state information of the object model may include pose information and surface material information. Optionally, the pose information of the object model includes the position of the object model, the attitude of the object model, and the scaling factor of the object model. Among them, the scaling factor of the object model is the ratio of the original length of the object model in each axis to the displayed length in the corresponding axis. The surface material information of the object model includes: the color information of the surface material of the object model and the texture information of the surface material.

[0121] After the graphics application 301 creates a 3D scene, it loads one or more object models in the 3D scene into the rendering engine module 302 through the rendering engine interface.

[0122] As Figure 5 shown, the renderer module renders an image according to the 3D scene created by the graphics application.

[0123] The renderer module 3022 obtains one or more object models to be rendered in the 3D scene according to the camera parameters, and renders the one or more object models to be rendered to obtain an image. Among them, the camera parameters include the position parameters, attitude parameters, viewport parameters, and field of view (FOV) parameters of the camera. The image rendered by the renderer module 3022 is an image without a bloom effect.

[0124] Figure 3 The functions of the scene management module 3021 shown in , the brightness area class change judgment module 30211 included therein, the post-processing glare effect module 3023, and the optimized large-dimension highlighted area glare algorithm module 30231 included therein are described in the following method embodiments.

[0125] It should be understood that the above Figure 2 and Figure 3 The software and hardware shown are only examples. In other embodiments, other types of software or hardware may be used.

[0126] Figure 6 is a flowchart of an image glare processing method provided by an embodiment of the present application. It can be applied to the above Figure 2 or Figure 3 shown electronic devices. As Figure 6 shown, the method includes:

[0127] Step 601, the electronic device performs brightness filtering processing on the first image.

[0128] When the electronic device performs brightness filtering processing on the first image, that is, pixels with pixel values less than the brightness threshold in the first image are removed to retain pixels with pixel values greater than or equal to the brightness threshold in the first image. Optionally, the brightness threshold may be a fixed value, that is, the brightness threshold for each image is the same. Or, the brightness threshold may be determined based on the pixel values of the pixels in the first image, so the brightness thresholds for different images may be different. Among them, the brightness threshold may be determined by the electronic device based on the pixel values of the pixels in the first image, or may be determined by other devices based on the pixel values of the pixels in the first image and then sent to the electronic device. The embodiments of the present application do not limit this.

[0129] Optionally, the process of determining the brightness threshold based on the pixel values of the pixels in the first image includes: dividing the pixels in the first image into multiple pixel value intervals according to the size of the pixel values. Any two pixel value intervals among the multiple pixel value intervals have no intersection, and the union of the multiple pixel value intervals is the complete set of pixel values. Obtain a target pixel value interval from the multiple pixel value intervals. The maximum value of the target pixel value interval is the first value, and the minimum value of the target pixel value interval is the second value. Then it satisfies: the sum of the number of pixels with pixel values less than the first value in the first image is greater than or equal to a preset number threshold, and the sum of the number of pixels with pixel values less than the second value in the first image is less than the preset number threshold. Use the minimum value (i.e., the second value), the maximum value (i.e., the first value), the average value, or the intermediate value of the target pixel value interval as the brightness threshold of the first image. Of course, the brightness threshold of the first image may also be other pixel values within the target pixel value interval. Among them, the preset number threshold may be 90% of the total number of pixels in the first image.

[0130] For example, assume that the above brightness threshold is the average value of the target pixel value range. The total number of pixels in the first image is 1920×1080. The multiple pixel value ranges are respectively: [0, 7], [8, 15], [16, 23], [24, 31],..., [240, 247], [248, 255], and the number of pixels in the first image divided into these multiple pixel value ranges is in sequence: 1280, 3840, 1920, 4800,..., 8640, 10368, 2880. If the sum of the number of all pixels in the pixel value ranges [0, 7], [8, 15], [16, 23], [24, 31],..., [224, 231] is less than 90% of the total number of pixels in the first image, and the sum of the number of all pixels in the pixel value ranges [0, 7], [8, 15], [16, 23], [24, 31],..., [224, 231], [232, 239] is greater than or equal to 90% of the total number of pixels in the first image, then the electronic device determines that the pixel value range [232, 239] is the target pixel value range, and further, the brightness threshold can be calculated as: (232 + 233 +... + 238 + 239) / 8 = 235.5.

[0131] Step 602: The electronic device obtains the first brightness region class of the first image, and the first brightness region class includes one or more brightness regions of the first image.

[0132] The first image may include one or more brightness region classes, and each brightness region class may include different one or more brightness regions in the first image. A brightness region may also be referred to as a highlight region, which refers to a region where the brightness value is greater than or equal to the brightness threshold. For example, if the first image is rendered based on a first 3D scene, then the above one or more brightness regions may be highlight regions on one or more object models in the first 3D scene. The first brightness region class may be any brightness region class in the first image.

[0133] Step 603: When the first brightness region class of the first image is the same as the target brightness region class of the second image after floodlight processing, the electronic device obtains a first intermediate image obtained by performing Gaussian blur processing on the target brightness region class of the second image.

[0134] Optionally, when the first image is rendered based on a first 3D scene and the second image is rendered based on a second 3D scene, then the first brightness region class of the first image being the same as the target brightness region class of the second image includes: the state information of the object models corresponding to the brightness regions in the first brightness region class in the first 3D scene is the same as the state information of the object models corresponding to the brightness regions in the target brightness region class in the second 3D scene, and the camera parameters of the first 3D scene are the same as the camera parameters of the second 3D scene.

[0135] Optionally, Figure 7 is a flowchart of a method for determining whether the first brightness region class of a first image is the same as the target brightness region class of a second image provided by an embodiment of the present application. As Figure 7 shown, this process includes:

[0136] Step 6031: The electronic device obtains the state information of all object models corresponding to the first brightness region class of the first image in the first 3D scene, and the state information of all object models corresponding to the target brightness region class of the second image in the second 3D scene.

[0137] Optionally, the state information of the object model may include pose information and surface material information. Among them, the pose information may include the position of the object model, the pose of the object model, and the scaling factor of the object model. The surface material information may include: the color information of the surface material of the object model and the texture information of the surface material.

[0138] Exemplarily, assume that the first brightness region class of the first image includes brightness region a, brightness region b, and brightness region c. Among them, brightness region a is the highlighted region of wall A, brightness region b is the highlighted region of mountain B, and brightness region c is the highlighted region of sky C. Then the electronic device obtains the state information of wall A, mountain B, and sky C in the first 3D scene and the second 3D scene.

[0139] Step 6032: The electronic device determines whether the state information of all object models corresponding to the first brightness region class in the first 3D scene is the same as the state information of all object models corresponding to the target brightness region class in the second 3D scene. When the state information of all object models corresponding to the first brightness region class is the same as the state information of all object models corresponding to the target brightness region class, step 6033 is executed; when the state information of all object models corresponding to the first brightness region class is not completely the same as the state information of all object models corresponding to the target brightness region class, step 6036 is executed.

[0140] Among them, all object models corresponding to the brightness region class include object models corresponding to each brightness region in the brightness region class. For the sake of convenience of description, in the embodiments of the present application, the brightness regions in the first brightness region class are referred to as first brightness regions, and the brightness regions in the target brightness region class are referred to as second brightness regions. The state information of all object models corresponding to the first brightness region class in the first 3D scene being the same as the state information of all object models corresponding to the target brightness region class in the second 3D scene means that the first brightness regions in the first brightness region class and the second brightness regions in the target brightness region class correspond one by one, and the state information of the object model corresponding to each first brightness region is the same as the state information of the object model corresponding to the corresponding second brightness region.

[0141] Exemplarily, please refer to the example in step 6031 above. When the status information of wall A in the first 3D scene is the same as the status information of wall A in the second 3D scene, the status information of mountain B in the first 3D scene is the same as the status information of mountain B in the second 3D scene, and the status information of sky C in the first 3D scene is the same as the status information of sky C in the second 3D scene, the electronic device determines that the status information of all object models corresponding to the first brightness region class in the first 3D scene is the same as the status information of all object models corresponding to the target brightness region class in the second 3D scene. When the status information of wall A in the first 3D scene is different from the status information of wall A in the second 3D scene, the status information of mountain B in the first 3D scene is different from the status information of mountain B in the second 3D scene, and / or the status information of sky C in the first 3D scene is different from the status information of sky C in the second 3D scene, the electronic device determines that the status information of all object models corresponding to the first brightness region class in the first 3D scene is not completely the same as the status information of all object models corresponding to the target brightness region class in the second 3D scene.

[0142] Step 6033: The electronic device obtains the camera parameters of the first 3D scene and the camera parameters of the second 3D scene.

[0143] Optionally, the camera parameters include: the pose parameters of the camera, the window parameters, and the FOV parameters.

[0144] Step 6034: The electronic device determines whether the camera parameters of the first 3D scene are the same as the camera parameters of the second 3D scene. When the camera parameters of the first 3D scene are the same as the camera parameters of the second 3D scene, step 6035 is executed; when the camera parameters of the first 3D scene are different from the camera parameters of the second 3D scene, step 6036 is executed.

[0145] Step 6035: The electronic device determines that the first brightness region class is the same as the target brightness region class.

[0146] In the embodiment of the present application, when the first brightness region class of the first image is the same as the target brightness region class of the second image after floodlight processing, the electronic device can directly obtain the first intermediate image obtained by performing Gaussian blur processing on the target brightness region, and use the first intermediate image as the image obtained by performing Gaussian blur processing on the first brightness region class of the first image. At this time, when the electronic device with the image floodlight processing function is running, there is no need to perform Gaussian blur processing on the first brightness region class of the first image, reducing the running load of the electronic device and reducing the power consumption of the electronic device.

[0147] Step 6036: The electronic device determines that the first brightness region class is different from the target brightness region class of the second image.

[0148] When the status information of all object models corresponding to the first brightness region class and the status information of all object models corresponding to the target brightness region class are not exactly the same, and / or the camera parameters of the first 3D scene and the camera parameters of the second 3D scene are different, the electronic device determines that the first brightness region class has changed relative to the target brightness region class, that is, the electronic device determines that the first brightness region class is different from the target brightness region class of the second image.

[0149] Optionally, when the first brightness region class is different from the target brightness region class, the electronic device performs Gaussian blur processing on the first brightness region class of the first image to obtain a corresponding intermediate image.

[0150] Optionally, the execution order of the above steps 6032 and 6034 can be swapped, that is, the electronic device can first execute step 6034 and then execute step 6032; or, the above steps 6032 and 6034 can also be executed simultaneously, and the embodiments of the present application do not limit this.

[0151] In the embodiments of the present application, the first brightness region class of the first image being the same as the target brightness region class of the second image further includes: the number of brightness regions in the first brightness region class is the same as the number of brightness regions in the target brightness region class; the types of object models corresponding to the brightness regions in the first brightness region class in the first 3D scene are the same as the types of object models corresponding to the brightness regions in the target brightness region class in the second 3D scene. Among them, the types of object models may include natural scenery (such as mountains and sky, etc.), buildings, plants, animals, etc. Then, the process of determining whether the first brightness region class of the first image is the same as the target brightness region class of the second image after floodlight processing may further include: the electronic device determines whether the number of brightness regions in the first brightness region class is the same as the number of brightness regions in the target brightness region class, and whether the types of object models corresponding to the brightness regions in the first brightness region class in the first 3D scene are the same as the types of object models corresponding to the brightness regions in the target brightness region class in the second 3D scene.

[0152] Optionally, in the embodiments of the present application, the electronic device may first determine whether the number of luminance regions in the first luminance region class is the same as the number of luminance regions in the target luminance region class, and whether the type of the object model corresponding to the luminance region in the first luminance region class in the first 3D scene is the same as the type of the object model corresponding to the luminance region in the target luminance region class in the second 3D scene; after determining that the number of luminance regions in the first luminance region class is the same as the number of luminance regions in the target luminance region class, and the type of the object model corresponding to the luminance region in the first luminance region class in the first 3D scene is the same as the type of the object model corresponding to the luminance region in the target luminance region class in the second 3D scene, then perform the above steps 6031 to 6036 to improve the judgment efficiency.

[0153] Optionally, when both the first image and the second image are images captured by a physical camera, the electronic device may, after obtaining the first luminance region class of the first image, obtain the target luminance region class of the second image, and compare the first luminance region class and the second luminance region class based on image processing technology to determine whether the first luminance region class is the same as the second luminance region class. Of course, when both the first image and the second image are images rendered based on a 3D scene, this method may also be used to determine whether the first luminance region class is the same as the second luminance region class, and the embodiments of the present application do not limit this.

[0154] Step 604: The electronic device generates a glare image of the first image based on the first image and the first intermediate image.

[0155] Optionally, the first image includes one or more luminance region classes.

[0156] The first case: When the first image includes one luminance region class, that is, the first image only includes the first luminance region class, the implementation process of step 604 includes: The electronic device performs image fusion processing on the first image and the first intermediate image to obtain a glare image of the first image.

[0157] In the embodiments of the present application, when the first image only includes the first luminance region class, and this first luminance region class is the same as the target luminance region class of the second image after glare processing, the electronic device may obtain a first intermediate image obtained by performing Gaussian blur processing on the target luminance region class, and generate a glare image of the first image based on the first image and this first intermediate image. On the premise of ensuring the glare effect of the first image, there is no need to perform Gaussian blur processing on the first image, significantly reducing the complexity of the image glare processing process, thereby reducing the load of the electronic device during operation after enabling this glare processing function, and reducing the power consumption of the electronic device.

[0158] Second case: When the first image includes multiple brightness region classes, the electronic device may separately perform the above steps 602 to 604 for each brightness region class in the first image to obtain an intermediate image corresponding to each brightness region class. Then, the implementation process of step 604 includes: The electronic device performs image fusion processing on the first image and the intermediate images corresponding to the respective brightness region classes of the first image to obtain a floodlight image of the first image.

[0159] Optionally, there are multiple ways to divide the brightness region classes in the image. The embodiments of the present application provide two ways to divide the brightness region classes, and take the first image including two brightness region classes (the first brightness region class and the second brightness region class) as an example to illustrate the process of the electronic device performing floodlight processing on the first image under different division methods. Among them, both the first brightness region class and the second brightness region class include one or more brightness regions of the first image, and the brightness regions in the first brightness region class are different from the brightness regions in the second brightness region class. Of course, the first image may also include three, four or even more brightness region classes, and the embodiments of the present application do not limit this.

[0160] In the first implementable manner, the brightness region classes are divided based on the size of the brightness regions. The sizes of the brightness regions in the first brightness region class are all larger than the sizes of the brightness regions in the second brightness region class. Or, the sizes of the brightness regions in the first brightness region class are all smaller than the sizes of the brightness regions in the second brightness region class. The embodiments of the present application take the sizes of the brightness regions in the first brightness region class being all larger than the sizes of the brightness regions in the second brightness region class as an example for illustration.

[0161] Optionally, the electronic device may store a preset size threshold. When the ratio of the size of the brightness region to the size of the image is greater than the size threshold, the electronic device divides the brightness region into one brightness region class; when the ratio of the size of the brightness region to the size of the image is less than or equal to the size threshold, the electronic device divides the brightness region into another brightness region class. Optionally, the value range of the size threshold may be 3% to 5% of the image size. For example, the size threshold may be 3%, 4% or 5% of the image size.

[0162] Optionally, Figure 8 is a flowchart of another image floodlight processing method provided by the embodiments of the present application. As Figure 8 shown, the method includes:

[0163] Step 801, the electronic device performs brightness filtering processing on the first image.

[0164] For the explanation of this step, reference may be made to the above step 601, and the embodiments of the present application will not elaborate here.

[0165] Step 802: The electronic device obtains the first brightness region class of the first image.

[0166] Step 803: The electronic device determines whether the first brightness region class of the first image is the same as the target brightness region class of the second image that has undergone floodlight processing; when the first brightness region class is the same as the target brightness region class, step 804 is executed; when the first brightness region class is different from the target brightness region class, step 805 is executed.

[0167] Optionally, after the electronic device obtains the first brightness region class of the first image, it determines the target brightness region class of the second image. The second image includes two brightness region classes, and the size of the brightness regions in the target brightness region class is greater than the size of the brightness regions in the other brightness region class in the second image.

[0168] Step 804: The electronic device obtains the first intermediate image obtained by performing Gaussian blur processing on the target brightness region class of the second image, and uses the first intermediate image as the intermediate image corresponding to the first brightness region class.

[0169] For the explanations of the above steps 803 and 804, reference can be made to the above step 603, and the embodiments of the present application will not elaborate here. After the electronic device executes step 804, it executes step 806.

[0170] Step 805: The electronic device performs Gaussian blur processing on the first brightness region class of the first image to obtain the intermediate image corresponding to the first brightness region class.

[0171] In the embodiments of the present application, since the size of the brightness regions in the first brightness region class of the first image is greater than the size of the brightness regions in the second brightness region class, therefore, performing Gaussian blur processing on the first brightness region class can be considered as performing Gaussian blur processing on the large-size brightness regions in the first image. At this time, the electronic device can perform a reduction process with a relatively small reduction ratio on the resolution of the first image to eliminate the pixels of the brightness regions with relatively small sizes (i.e., the brightness regions of the detailed parts) in the first image, and only retain the pixels of the brightness regions with relatively large sizes (i.e., the brightness regions of the large dimensions), so as to facilitate the electronic device to perform Gaussian blur processing on the large-size brightness regions (the first brightness region class) of the first image.

[0172] Exemplarily, the process of performing Gaussian blur processing on the first brightness region class of the first image may include: The electronic device first performs reduced pixel sampling processing on the first image using the second reduction ratio to obtain the second reduced image for the first brightness region class. Then, Gaussian blur processing is performed on the second reduced image to obtain the intermediate image corresponding to the first brightness region class. Among them, the second reduction ratio k2 can satisfy: k2 = 2 m, where m is an integer and m < -3. In the embodiments of the present application, the second reduction ratio may refer to a single reduction ratio or a set of multiple reduction ratios. For example, if m can take the value of -4 or -5, then the second reduction ratio k2 includes 1 / 16 and 1 / 32.

[0173] Optionally, when the second reduction ratio includes two reduction ratios, the process of performing Gaussian blur processing on the first brightness region class of the first image is as follows: The electronic device performs downsampling processing on the first image using two different reduction ratios respectively to obtain two downsampled images for this first brightness region class. Then the electronic device performs Gaussian blur processing on the two downsampled images respectively to obtain two intermediate images. In the embodiments of the present application, the two reduction ratios included in the second reduction ratio may be 1 / 16 and 1 / 32 respectively.

[0174] Step 806: The electronic device obtains the second brightness region class of the first image.

[0175] Step 807: The electronic device determines whether there is a certain brightness region class in the second image after floodlight processing that is the same as the second brightness region class of the first image; when there is a certain brightness region class in the second image after floodlight processing that is the same as the second brightness region class of the first image, step 808 is executed; when any brightness region class in the second image after floodlight processing is not the same as the second brightness region class of the first image, step 809 is executed.

[0176] Optionally, after the electronic device obtains the second brightness region class of the first image, it obtains the brightness region class with a smaller size in the second image and determines whether the second brightness region class is the same as this smaller-size brightness region class.

[0177] Step 808: The electronic device obtains the intermediate image obtained after performing Gaussian blur processing on the brightness region class in the second image that is the same as the second brightness region class, and uses this intermediate image as the intermediate image corresponding to the second brightness region class.

[0178] The explanations of the above step 807 and step 808 can refer to the above step 603, and the embodiments of the present application will not elaborate here. After the electronic device executes step 808, it executes step 810.

[0179] Step 809: The electronic device performs Gaussian blur processing on the second brightness region class of the first image to obtain the intermediate image corresponding to the second brightness region class.

[0180] In the embodiments of the present application, the intermediate image obtained by performing Gaussian blur processing on the second luminance region class of the first image by the electronic device is referred to as the second intermediate image. Since the sizes of the luminance regions in the second luminance region class of the first image are all smaller than the sizes of the luminance regions in the first luminance region class, therefore, performing Gaussian blur processing on the second luminance region class can be considered as performing Gaussian blur processing on the small-sized luminance regions in the first image. At this time, the electronic device can perform a reduction process with a large reduction ratio on the resolution of the first image, and can retain the pixels of the small-sized luminance regions (i.e., the luminance regions of the detailed parts) in the first image, so as to be able to implement the light flooding processing on the small-sized luminance regions of the first image. In addition, since the electronic device can retain the small-sized luminance regions in the first image after performing a reduction process with a large reduction ratio on the resolution of the first image, a small-sized convolution kernel can be used to perform Gaussian blur processing on the reduced first image. Since the larger the size of the convolution kernel, the higher the complexity of the Gaussian blur processing, therefore, the complexity of performing Gaussian blur processing using a small-sized convolution kernel in the embodiments of the present application is relatively low, making the complexity of the image light flooding processing process relatively low.

[0181] Exemplarily, the process of performing Gaussian blur processing on the second luminance region class of the first image may include: The electronic device first performs a reduction pixel sampling process on the first image using a first reduction ratio to obtain a first reduced image for the second luminance region class. Then, Gaussian blur processing is performed on the first reduced image to obtain a second intermediate image. Wherein, the first reduction ratio k1 may satisfy: k1 = 2 m , m is an integer, and -3 ≤ n ≤ 0. In the embodiments of the present application, the first reduction ratio may refer to a single reduction ratio or a set of multiple reduction ratios. For example, if n can take the values -2 or -3, then the first reduction ratio k1 includes 1 / 4 and 1 / 8.

[0182] Optionally, when the first reduction ratio includes two reduction ratios, the above process of performing Gaussian blur processing on the second luminance region class of the first image includes: The electronic device performs reduction pixel sampling processes on the first image using two different reduction ratios respectively to obtain two reduced images for the second luminance region class. Then, the electronic device performs Gaussian blur processing on the two reduced images respectively to obtain two intermediate images. In the embodiments of the present application, the two reduction ratios included in the first reduction ratio may be 1 / 4 and 1 / 8 respectively.

[0183] Step 810, the electronic device performs image fusion processing on the first image, the intermediate image corresponding to the first luminance region class, and the intermediate image corresponding to the second luminance region class to obtain a light flooded image of the first image.

[0184] Optionally, the electronic device may not perform the above steps 807 to 808. That is, after the electronic device finishes performing the above step 806, it may directly perform Gaussian blur processing on the second brightness region class of the first image to obtain a second intermediate image, thereby ensuring the bloom effect of the first image.

[0185] In the second implementation manner, the brightness region class is divided based on the category of the object model corresponding to the brightness region of the image in the 3D scene.

[0186] In a 3D scene such as a game or an animation, it usually includes a foreground object model (also known as a character foreground) and a background object model (also known as a background object). Among them, the foreground object model usually has characteristics such as being movable, having a small volume, and having a high change frequency in consecutive multiple frames of images. For example, the foreground object model includes object models such as a person or an animal. Since the volume of the foreground object model is usually small, it can be considered that the size of the highlighted area on the foreground object model is usually small, that is, most of the highlighted areas on the foreground object model are small detail parts. The background object model usually has characteristics such as being immovable, having a large volume, and having a low change frequency in consecutive multiple frames of images. For example, the background object model includes object models such as the sky, mountains, and buildings. Since the volume of the background object model is usually large, it can be considered that the size of the highlighted area on the background object model is usually large, that is, most of the highlighted areas on the background object model are large-dimensional parts with large sizes.

[0187] Exemplarily, Figure 9 is a schematic diagram of a 3D game interface (i.e., one frame of an image) provided by an embodiment of the present application. As Figure 9 shown, the 3D scene corresponding to the 3D game interface at least includes: a person R belonging to the foreground object model, and a house wall P and a spotlight Q belonging to the background object model. Among them, the light emitted by the spotlight Q irradiates on the person R and the house wall P. The light irradiating on the person R converges into a fluorescent circle, forming a highlighted area M1 with a small size on the person R (i.e., the shadow area on the person R in the figure). The light irradiating on the house wall P forms a brightness area M2 with a large size on the house wall P (the shadow area on the house wall P in the figure).

[0188] Since the background object model has the characteristic of low change frequency in consecutive multiple frames of images, in the image rendered based on the 3D scene, the brightness area of the background object model in the current frame image has a high probability of being the same as the brightness area in the previous frame image after bloom processing. Therefore, in the embodiments of the present application, before performing bloom processing on the current frame image, it can be first determined whether the background object model in the 3D scene corresponding to the current frame image has changed relative to the background object model in the 3D scene corresponding to the previous frame image. If there is no change, there is no need to perform Gaussian blur processing on the brightness area corresponding to the background object model in the current frame image, but directly obtain the intermediate image obtained by performing Gaussian blur processing on the brightness area corresponding to the background object model in the previous frame image, thereby reducing the number of times of performing Gaussian blur processing on the image, and further reducing the complexity of the image bloom processing process.

[0189] In the embodiments of the present application, the electronic device may use the set composed of the background brightness areas corresponding to all background object models in the 3D scene in the image as the first brightness area class. That is, the first brightness area class includes the background brightness areas corresponding to all background object models in the 3D scene corresponding to the image, and this first brightness area class may also be referred to as the background class. The set composed of the brightness areas corresponding to other object models in the 3D scene except the background object model in the image is used as the second brightness area class. That is, the second brightness area class includes other brightness areas in the image except the background brightness area. Other object models except the background object model include foreground object models, and this second brightness area class may also be referred to as the foreground class, and the brightness areas in the second brightness area class may be referred to as foreground brightness areas.

[0190] Optionally, Figure 10 is a flowchart of another image bloom processing method provided by the embodiments of the present application. As Figure 10 shown, the method includes:

[0191] Step 1001: The electronic device performs brightness filtering processing on the first image.

[0192] For the explanation of this step, reference can be made to the above step 601, and the embodiments of the present application will not elaborate here.

[0193] Step 1002: The electronic device performs Gaussian blur processing on the foreground class of the first image to obtain an intermediate image corresponding to the foreground class of the first image.

[0194] In an embodiment of the present application, an intermediate image obtained by performing Gaussian blur processing on the foreground class of the first image by an electronic device is referred to as a second intermediate image. The size of the bright region in the foreground class is generally smaller than the size of the bright region in the background class. The manner in which the electronic device performs Gaussian blur processing on the foreground class of the first image may refer to the process of performing Gaussian blur processing on a small-sized bright region in step 809 above, and the embodiments of the present application will not elaborate on this.

[0195] Step 1003: The electronic device acquires a background class object model in the first 3D scene.

[0196] The first image is rendered based on the first 3D scene. Optionally, when an image is rendered based on a 3D scene, it can be determined whether the background class of the image is the same as the background class of the image corresponding to the 3D scene after bloom processing by determining whether the background class object model of the 3D scene corresponding to the image is the same as the background class object model of the 3D scene corresponding to the image after bloom processing. Therefore, the electronic device acquiring the background class of the image can be replaced with: the electronic device acquires the background class object model in the 3D scene corresponding to the image.

[0197] Optionally, the process by which the electronic device acquires the background class object model in the first 3D scene may include: the electronic device traverses the labels of all object models in the first 3D scene to acquire all background class object models in the first 3D scene, and the first bright region class includes the background class bright regions corresponding to all background class object models in the first image. This label is used to indicate whether the object model is a background class object model. In an embodiment of the present application, optionally, each object model in the 3D scene may carry a label, and this label is used to indicate whether the object model belongs to the background class object model or the foreground class object model. This label may be manually marked or automatically divided by the electronic device according to the category of the object model. The label may be represented by a numerical value, a letter, a string, etc. By way of example, when the label of the object model is "0", it indicates that the object model belongs to the background class object model; when the label of the object model is "1", it indicates that the object model belongs to the foreground class object model.

[0198] Step 1004: The electronic device determines whether the background class object model in the first 3D scene is the same as the background class object model in the second 3D scene; when the background class object model in the first 3D scene is the same as the background class object model in the second 3D scene, step 1005 is executed; when the background class object model in the first 3D scene is different from the background class object model in the second 3D scene, step 1006 is executed.

[0199] The electronic device determines whether the background object model in the first 3D scene is the same as the background object model in the second 3D scene, that is, determines whether the background object model in the first 3D scene has changed relative to the background object model in the second 3D scene.

[0200] Step 1005: The electronic device obtains a first intermediate image obtained by performing Gaussian blur processing on the background class of the second image, and uses this first intermediate image as the intermediate image corresponding to the background class of the first image.

[0201] The second image is rendered based on the second 3D scene. When the background object model in the first 3D scene is the same as the background object model in the second 3D scene, the electronic device determines that the background object model in the first 3D scene corresponding to the first image has not changed relative to the background object model in the second 3D scene corresponding to the second image. Therefore, it can be considered that the background class of the first image is the same as the background class of the second image.

[0202] In the embodiments of the present application, the process by which the electronic device determines whether the background object model in the first 3D scene is the same as the background object model in the second 3D scene is the same as the process in step 603 above, in which the electronic device determines whether the first brightness region class of the first image is the same as the target brightness region class of the second image. Therefore, the explanations of steps 1004 and 1005 above can refer to step 603 above, and the embodiments of the present application will not elaborate here. After the electronic device executes step 1005, it executes step 1007.

[0203] Step 1006: The electronic device performs Gaussian blur processing on the background class of the first image to obtain an intermediate image corresponding to the background class of the first image.

[0204] When the background object model in the first 3D scene is different from the background object model in the second 3D scene, the electronic device determines that the background object model in the first 3D scene corresponding to the first image has changed relative to the background object model in the second 3D scene corresponding to the second image. Therefore, it can be considered that the background class of the first image is different from the background class of the second image. In the embodiments of the present application, since the size of the brightness region in the background class is usually larger than the size of the brightness region in the foreground class, the method by which the electronic device performs Gaussian blur processing on the background class of the first image can refer to the process of Gaussian blur processing for large-size brightness regions in step 805 above, and the embodiments of the present application will not elaborate here.

[0205] Step 1007: The electronic device performs image fusion processing on the first image, the intermediate image corresponding to the background class of the first image, and the intermediate image corresponding to the foreground class of the first image to obtain a glare image of the first image.

[0206] In the embodiments of the present application, the above step 1003 may be executed before step 1001, or may also be executed simultaneously with step 1001. For example, while rendering the first image according to the first 3D scene, the electronic device determines whether the background object models in the first 3D scene are the same as the background object models in the second 3D scene. Or, while performing brightness filtering on the first image, the electronic device determines whether the background object models in the first 3D scene are the same as the background object models in the second 3D scene.

[0207] Optionally, in another image bloom processing method provided by the embodiments of the present application, the electronic device may also determine whether the foreground object models in the first 3D scene are the same as the foreground object models in the second 3D scene, so that when the foreground object models in the first 3D scene are the same as the foreground object models in the second 3D scene, the electronic device obtains an intermediate image corresponding to the foreground of the first image obtained by performing Gaussian blur processing on the foreground of the second image. When the foreground object models in the first 3D scene are different from the foreground object models in the second 3D scene, the electronic device performs the above step 1002 to perform Gaussian blur processing on the foreground of the first image to obtain an intermediate image corresponding to the foreground of the first image. In this way, when the foreground object models in the first 3D scene are the same as the foreground object models in the second 3D scene, an intermediate image corresponding to the foreground of the first image obtained by performing Gaussian blur processing on the foreground of the second image is obtained, without performing Gaussian blur processing on the foreground of the first image, reducing the number of times of performing Gaussian blur processing on the first image, and thus reducing the complexity of the image bloom processing process.

[0208] The following embodiments of the present application exemplarily illustrate the process of the electronic device implementing the image bloom processing method as Figure 10 shown. Exemplarily, Figure 11 is a schematic flowchart of an electronic device implementing an image bloom processing method provided by the embodiments of the present application. As Figure 11 shown, the object models in the first 3D scene include object 1, object 2, object 3, object 4, object 5, and object 6. Among them, object 1, object 2, and object 3 have the same label of background label, and this background label is used to indicate that the object model belongs to the background object model. Object 4, object 5, and object 6 have the same label of foreground label (also called character label), and this foreground label is used to indicate that the object model belongs to the foreground object model.

[0209] All object models in the first 3D scene are stored in the scene management module of the electronic device.

[0210] The renderer module renders the first image according to the first 3D scene.

[0211] The optimized large-dimension highlight area bloom algorithm module performs downsampling on the first image at a reduction ratio of 1 / 4 to obtain a downsampled image with a resolution that is 1 / 4 of the original resolution (the resolution of the first image), which is abbreviated as the 1 / 4 downsampled image. Then, Gaussian blur processing is performed on the 1 / 4 downsampled image to obtain an intermediate image corresponding to the foreground class of the first image (abbreviated as the 1 / 4 Gaussian blur result image). Additionally, downsampling is performed on the first image at a reduction ratio of 1 / 8 to obtain a downsampled image with a resolution that is 1 / 8 of the original resolution (abbreviated as the 1 / 8 downsampled image). Then, Gaussian blur processing is performed on the 1 / 8 downsampled image to obtain another intermediate image corresponding to the foreground class of the first image (abbreviated as the 1 / 8 Gaussian blur result image).

[0212] The brightness region class change determination module traverses objects 1 to 6 in the scene management module, determines objects 1 to 3 with background labels, and determines whether objects 1, 2, and 3 in the first 3D scene are the same as the background class object models in the second 3D scene, respectively. When objects 1, 2, and 3 in the first 3D scene are all the same as the background class object models in the second 3D scene, respectively, the optimized large-dimension highlight area bloom algorithm module obtains the intermediate images after Gaussian blur processing for the second image (i.e., the 1 / 16 Gaussian blur result image and the 1 / 32 Gaussian blur result image shown in the figure). When objects 1, 2, and 3 in the first 3D scene are different from the background class object models in the second 3D scene, the optimized large-dimension highlight area bloom algorithm module performs Gaussian blur processing on the background class of the first image to obtain an intermediate image corresponding to the background class of the first image.

[0213] Among them, the process of the optimized large-dimension highlight area bloom algorithm module performing Gaussian blur processing on the background class of the first image includes: the optimized large-dimension highlight area bloom algorithm module performs downsampling on the first image at a reduction ratio of 1 / 16 to obtain a downsampled image with a resolution that is 1 / 16 of the original resolution (abbreviated as the 1 / 16 downsampled image). Then, Gaussian blur processing is performed on the 1 / 16 downsampled image to obtain an intermediate image corresponding to the background class of the first image (abbreviated as the 1 / 16 Gaussian blur result image). Additionally, downsampling is performed on the first image at a reduction ratio of 1 / 32 to obtain a downsampled image with a resolution that is 1 / 32 of the original resolution (abbreviated as the 1 / 32 downsampled image). Then, Gaussian blur processing is performed on the 1 / 32 downsampled image to obtain another intermediate image corresponding to the background class of the first image (abbreviated as the 1 / 32 Gaussian blur result image).

[0214] The process of the optimized large - dimension highlight area bloom algorithm module for obtaining the intermediate image after Gaussian blur processing on the second image includes: the optimized large - dimension highlight area bloom algorithm module directly obtains two intermediate images corresponding to the background class of the second image: the 1 / 16 Gaussian blur result image and the 1 / 32 Gaussian blur result image.

[0215] The rendering engine module performs image fusion processing on the first image, the 1 / 4 Gaussian blur result image, the 1 / 8 Gaussian blur result image, the 1 / 16 Gaussian blur result image, and the 1 / 32 Gaussian blur result image to obtain the bloom image of the first image.

[0216] Please refer to Table 1. Assume that the electronic device is a mobile terminal. Table 1 records: when the electronic device with the image bloom processing function enabled runs a game application with relatively high image quality (such as a game application with a frame rate of 60), the CPU and GPU load conditions and the system power consumption when using the image bloom processing method provided by the related technology (abbreviated as the related - technology algorithm), and the CPU and GPU load conditions and the system power consumption when using the image bloom processing method as Figure 11 shown (abbreviated as the algorithm of this application).

[0217] Table 1

[0218] Related technical algorithms The algorithm of this application GPU load increase rate 5％ 4％ CPU load increase rate 12％ 8％ Power consumption increase (milliamperes / mA) 103 71

[0219] As can be seen from Table 1, during the process of the electronic device running the above - mentioned game application with the related algorithm enabled, the GPU load increase rate of the electronic device is 5%, and the CPU load increase rate is 12%. While during the process of the electronic device running the above - mentioned game application with the algorithm of this application enabled, the GPU load increase rate of the electronic device is 4%, and the CPU load increase rate is 8%. During the process of the electronic device running the above - mentioned game application with the related algorithm enabled, the power consumption of the electronic device increases by 103 mA. While during the process of the electronic device running the above - mentioned game application with the algorithm of this application enabled, the power consumption of the electronic device increases by 71 mA. Therefore, compared with the related technology, the electronic device adopting the Figure 11 shown image bloom processing method can significantly reduce the load and power consumption of the electronic device.

[0220] In the embodiment of this application, each step in the above - mentioned image bloom processing method can be executed by Figure 3 the same or different modules in the shown electronic device.

[0221] Exemplarily, the rendering engine module 302 can be used to execute the above - mentioned step 601, step 801, and step 1001.

[0222] The optimized large - dimension highlight area bloom algorithm module 30231 can be used to execute the above steps 603, 804, 805, 808, 809, 1002, 1005, and 1006.

[0223] The brightness area class change judgment module 30211 can be used to execute the above steps 602, 802, 803, 806, 807, 1003, 1004, and steps 6031 to 6036.

[0224] The post - processing bloom effect module 2023 can be used to execute steps 604, 810, and 1007.

[0225] The rendering engine module 302 is also used to present the bloom - processed image of the first image generated by the optimized large - dimension highlight area bloom algorithm module 30231 on the display device of the terminal by calling the OpenGL ES interface layer 3031 or calling the Vulkan interface layer 3032.

[0226] In summary, for the image bloom - processing method provided in the embodiments of the present application, since the electronic device can directly obtain the intermediate image obtained by performing Gaussian blur processing on the target brightness area class of the second image when determining that the first brightness area class in the first image is the same as the target brightness area class of the second image after bloom processing, without performing Gaussian blur processing on the first brightness area class of the first image. Therefore, under the premise of ensuring the bloom effect of the first image, the embodiments of the present application can reduce the number of times of Gaussian blur processing on the first image, thereby reducing the complexity of the image bloom - processing process, reducing the load of the electronic device during operation after enabling the bloom - processing function, and reducing the power consumption of the electronic device.

[0227] In addition, when the first image includes multiple brightness area classes, the electronic device can also directly obtain the intermediate image obtained by performing Gaussian blur processing on the brightness area class in the second image that is the same as the second brightness area class in the first image when determining that there is a certain brightness area class in the second image after bloom processing that is the same as the second brightness area class in the first image, without performing Gaussian blur processing on the second brightness area class of the first image. Therefore, under the premise of ensuring the bloom effect of the first image, the embodiments of the present application can further reduce the number of times of Gaussian blur processing on the first image, thereby reducing the complexity of the image bloom - processing process, reducing the load of the electronic device during operation after enabling the bloom - processing function, and reducing the power consumption of the electronic device.

[0228] The following is an apparatus embodiment of the present application, which can be used to implement the method embodiment of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the method embodiment of the present application.

[0229] Please refer to Figure 12 , which shows a block diagram of an image floodlight processing apparatus provided in an embodiment of the present application. The apparatus 1200 may include:

[0230] A first acquisition module 1201, configured to acquire a first brightness region class of a first image, where the first brightness region class includes one or more brightness regions of the first image.

[0231] A second acquisition module 1202, configured to acquire a first intermediate image obtained by performing Gaussian blur processing on a target brightness region class of a second image after determining that the first brightness region class of the first image is the same as the target brightness region class of the second image that has undergone floodlight processing.

[0232] A generation module 1203, configured to generate a floodlight image of the first image based on the first image and the first intermediate image.

[0233] Optionally, the first image is rendered based on a first three-dimensional scene, the second image is rendered based on a second three-dimensional scene, and the first brightness region class is the same as the target brightness region class, including:

[0234] The state information of the object models corresponding to the brightness regions in the first brightness region class in the first three-dimensional scene is the same as the state information of the object models corresponding to the brightness regions in the target brightness region class in the second three-dimensional scene, and the camera parameters of the first three-dimensional scene are the same as the camera parameters of the second three-dimensional scene.

[0235] Optionally, as Figure 13 shown, the apparatus 1200 may further include:

[0236] A third acquisition module 1204, configured to acquire the state information of all object models corresponding to the first brightness region class in the first three-dimensional scene, and the state information of all object models corresponding to the target brightness region class in the second three-dimensional scene.

[0237] A fourth acquisition module 1205, configured to acquire the camera parameters of the first three-dimensional scene and the camera parameters of the second three-dimensional scene after determining that the state information of all object models corresponding to the first brightness region class in the first three-dimensional scene is the same as the state information of all object models corresponding to the target brightness region class in the second three-dimensional scene.

[0238] A determination module 1206, configured to determine that the first brightness region class is the same as the target brightness region class when the camera parameters of the first three-dimensional scene are the same as the camera parameters of the second three-dimensional scene.

[0239] Optionally, the state information of the object model includes: the pose information and surface material information of the object model, and the camera parameters include: the pose parameters, viewport parameters, and field of view parameters of the camera.

[0240] Optionally, the first acquisition module 1201 is configured to: traverse the labels of all object models in the first three-dimensional scene, where the labels are used to indicate whether the object models are background object models; acquire all background object models in the first three-dimensional scene, and the first brightness region class includes the background brightness regions corresponding to all background object models in the first image.

[0241] Optionally, the first image further includes a second brightness region, and the second brightness region class includes other brightness regions in the first image except the background brightness regions. For example, Figure 14 as shown, the apparatus 1200 may further include:

[0242] A Gaussian blur processing module 1207, configured to perform Gaussian blur processing on the second brightness region class to obtain a second intermediate image. A generation module 1203 is configured to: perform image fusion processing on the first image, the first intermediate image, and the second intermediate image to obtain a bloom image of the first image.

[0243] Optionally, the first image further includes a second brightness region class, and the second brightness region class includes one or more brightness regions of the first image. Alternatively, the above Gaussian blur processing module 1207 is configured to, after determining that the second brightness region class of the first image is different from any brightness region class of the second image, perform Gaussian blur processing on the second brightness region class to obtain a second intermediate image. The generation module is configured to: perform image fusion processing on the first image, the first intermediate image, and the second intermediate image to obtain a bloom image of the first image.

[0244] Optionally, the sizes of the brightness regions in the first brightness region class are all larger than the sizes of the brightness regions in the second brightness region class. The Gaussian blur processing module 1207 is configured to: perform downsampling pixel sampling processing on the first image using a first reduction ratio to obtain a first reduced image; perform Gaussian blur processing on the first reduced image to obtain a second intermediate image;

[0245] Or, the sizes of the brightness regions in the first brightness region class are all smaller than the sizes of the brightness regions in the second brightness region class. The Gaussian blur processing module 1207 is configured to: perform downsampling pixel sampling processing on the first image using a second reduction ratio to obtain a second reduced image; perform Gaussian blur processing on the second reduced image to obtain a second intermediate image. Wherein, the first reduction ratio is greater than the second reduction ratio.

[0246] Optionally, the first reduction ratio k1 satisfies: k1 = 2 n, where n is an integer and -3 ≤ n ≤ 0; the second reduction ratio k2 satisfies: k2 = 2 m , where m is an integer and m < -3.

[0247] Optionally, as Figure 15 shown, the device 1200 may further include:

[0248] A filtering processing module 1208, configured to perform a brightness filtering process on the first image before generating a bloom image of the first image based on the first image and the first intermediate image.

[0249] Optionally, the first image and the second image are two consecutive frames of images, and the second image is the previous frame image of the first image.

[0250] In summary, for the image bloom processing device provided in the embodiments of the present application, since when it is determined that the first brightness region class in the first image is the same as the target brightness region class of the second image after bloom processing, the intermediate image obtained by directly performing Gaussian blur processing on the target brightness region class of the second image can be obtained, without performing Gaussian blur processing on the first brightness region class of the first image. Therefore, under the premise of ensuring the bloom effect of the first image, the embodiments of the present application can reduce the number of times of performing Gaussian blur processing on the first image, thereby reducing the complexity of the image bloom processing process, reducing the load during the operation of the image bloom processing device after the bloom processing function is turned on, and reducing the power consumption of the image bloom processing device.

[0251] In addition, when the first image includes multiple brightness region classes, the image bloom processing device may also directly obtain the intermediate image obtained by performing Gaussian blur processing on the brightness region class in the second image that is the same as the second brightness region class in the first image when it is determined that there is a certain brightness region class in the second image after bloom processing that is the same as the second brightness region class in the first image, without performing Gaussian blur processing on the second brightness region class of the first image. Therefore, under the premise of ensuring the bloom effect of the first image, the embodiments of the present application can further reduce the number of times of performing Gaussian blur processing on the first image, thereby reducing the complexity of the image bloom processing process, reducing the load during the operation of the image bloom processing device after the bloom processing function is turned on, and reducing the power consumption of the image bloom processing device.

[0252] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the devices and modules described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0253] Moreover, each module in the above device can be implemented by software or a combination of software and hardware. When at least one module is hardware, the hardware can be a logic integrated circuit module, which may specifically include transistors, logic gate arrays, or algorithmic logic circuits, etc. When at least one module is software, the software exists in the form of a computer program product and is stored in a computer-readable storage medium. The software can be executed by a processor. Therefore, alternatively, the image glare processing device can be implemented by a processor executing a software program, and this embodiment is not limited thereto.

[0254] An embodiment of the present application also provides an image glare processing device, as Figure 16 shown. The device includes a processor 1601 and a memory 1602; when the processor 1601 executes the computer program stored in the memory 1602, the image glare processing device executes the image glare processing method provided by the embodiment of the present application. Optionally, the image glare processing device can be deployed in a terminal.

[0255] Optionally, the device further includes a communication interface 1603 and a bus 1604. The processor 1601, the memory 1602, and the communication interface 1603 are communicatively connected through the bus 1604. Among them, there are multiple communication interfaces 1603, which are used to communicate with other devices under the control of the processor 1601; the processor 1601 can call the computer program stored in the memory 1602 through the bus 1604.

[0256] An embodiment of the present application also provides a storage medium, which can be a non-volatile computer-readable storage medium. The storage medium stores a computer program, and the computer program instructs a processing component to execute any one of the image glare processing methods provided by the embodiment of the present application. The storage medium can include various media that can store program codes, such as read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0257] The embodiments of the present application also provide a computer program product containing instructions. When the computer program product runs on a computer, it causes the computer to execute the image floodlight processing method provided by the embodiments of the present application. The computer program product may include one or more computer instructions. When the computer instructions are loaded and executed on the computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer-readable storage medium may be any available medium that the computer can access or a data storage device such as a server or a data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0258] The embodiments of the present application also provide a chip, such as a CPU chip. The chip includes one or more physical cores and a storage medium. After the one or more physical cores read the computer instructions in the storage medium, the foregoing image floodlight processing method is implemented. In other embodiments, the chip may implement the foregoing image floodlight processing method in a pure hardware or a combination of hardware and software manner, that is, the chip includes a logic circuit. When the chip runs, the logic circuit is used to implement any one of the image floodlight processing methods provided by the embodiments of the present application. The logic circuit may be a programmable logic circuit. Similarly, a GPU may also be implemented like a CPU.

[0259] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above embodiments can be completed by hardware or by a program instructing relevant hardware. The program may be stored in a computer-readable storage medium. The storage medium mentioned above may be a read-only memory, a magnetic disk, an optical disc, etc.

[0260] In the embodiments of the present application, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. The term "at least one" means one or more, and the term "a plurality" means two or more, unless otherwise clearly defined.

[0261] The term "and / or" in the present application is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0262] The foregoing are only alternative embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the concept and principle of the present application shall be included within the protection scope of the present application.

Claims

1. An image blooming processing method, characterized in that The method includes: Obtaining a first brightness region class of a first image, where the first brightness region class includes one or more brightness regions of the first image; After determining that the first brightness region class of the first image is the same as a target brightness region class of a second image that has been subjected to bloom processing, obtaining a first intermediate image obtained by performing Gaussian blur processing on the target brightness region class of the second image, where the second image is displayed before the first image; Generating a bloom image of the first image based on the first image and the first intermediate image.

2. The method according to claim 1, characterized in that The first image is rendered based on a first three-dimensional scene, and the second image is rendered based on a second three-dimensional scene. The first brightness region class being the same as the target brightness region class includes: The state information of the object models corresponding to the brightness regions in the first brightness region class in the first three-dimensional scene is the same as the state information of the object models corresponding to the brightness regions in the target brightness region class in the second three-dimensional scene, and the camera parameters of the first three-dimensional scene are the same as the camera parameters of the second three-dimensional scene.

3. The method according to claim 2, wherein The method further includes: Obtaining the state information of all object models corresponding to the first brightness region class in the first three-dimensional scene, and the state information of all object models corresponding to the target brightness region class in the second three-dimensional scene; After determining that the state information of all object models corresponding to the first brightness region class in the first three-dimensional scene is the same as the state information of all object models corresponding to the target brightness region class in the second three-dimensional scene, obtaining the camera parameters of the first three-dimensional scene and the camera parameters of the second three-dimensional scene; After determining that the camera parameters of the first three-dimensional scene are the same as the camera parameters of the second three-dimensional scene, determining that the first brightness region class is the same as the target brightness region class.

4. The method according to claim 2 or 3, characterized in that, The state information of the object model includes: the pose information and surface material information of the object model, and the camera parameters include: the pose parameters, window parameters, and field of view parameters of the camera.

5. The method according to claim 2 or 3, characterized in that, The obtaining of the first brightness region class of the first image includes: Traversing the labels of all object models in the first three-dimensional scene, where the labels are used to indicate whether the object models are background class object models; Obtaining all the background class object models in the first three-dimensional scene, and the first brightness region class includes the background class brightness regions corresponding to all the background class object models in the first image.

6. The method according to claim 5, wherein The first image further includes a second brightness region, and the second brightness region class includes other brightness regions in the first image except the background class brightness regions. The method further includes: Performing Gaussian blur processing on the second brightness region class to obtain a second intermediate image; The generating of the bloom image of the first image based on the first image and the first intermediate image includes: Performing image fusion processing on the first image, the first intermediate image, and the second intermediate image to obtain the bloom image of the first image.

7. The method according to claim 1, wherein The first image further includes a second brightness region class, where the second brightness region class includes one or more brightness regions of the first image, and the method further includes: After determining that the second brightness region class of the first image is different from any brightness region class of the second image, performing Gaussian blur processing on the second brightness region class to obtain a second intermediate image; Generating a bloom image of the first image based on the first image and the first intermediate image, including: Performing image fusion processing on the first image, the first intermediate image, and the second intermediate image to obtain the bloom image of the first image.

8. The method according to claim 6 or 7, characterized in that, The sizes of the brightness regions in the first brightness region class are all larger than the sizes of the brightness regions in the second brightness region class. Performing Gaussian blur processing on the second brightness region class of the first image includes: Performing sub-sampling processing on the first image at a first reduction ratio to obtain a first reduced image; Performing Gaussian blur processing on the first reduced image to obtain the second intermediate image; Alternatively, the sizes of the brightness regions in the first brightness region class are all smaller than the sizes of the brightness regions in the second brightness region class. Performing Gaussian blur processing on the second brightness region class of the first image includes: Performing sub-sampling processing on the first image at a second reduction ratio to obtain a second reduced image; Performing Gaussian blur processing on the second reduced image to obtain the second intermediate image; where the first reduction ratio is greater than the second reduction ratio.

9. The method according to claim 8, wherein The first reduction ratio k1 satisfies: k1 = 2 n , where n is an integer and -3 ≤ n ≤ 0; the second reduction ratio k2 satisfies: k2 = 2 m , where m is an integer and m < -3.

10. The method according to any one of claims 1 to 3 and 7, characterized in that Before generating the bloom image of the first image based on the first image and the first intermediate image, the method further includes: Performing brightness filtering processing on the first image.

11. According to the method described in any one of claims 1 to 3 and 7, characterized in that, The first image and the second image are two consecutive frames of images, and the second image is the previous frame image of the first image.

12. An image bloom processing device, characterized in that, The apparatus includes: A first acquisition module, configured to acquire a first brightness region class of a first image, where the first brightness region class includes one or more brightness regions of the first image; A second acquisition module, configured to acquire a first intermediate image obtained by performing Gaussian blur processing on a target brightness region class of a second image after determining that the first brightness region class of the first image is the same as the target brightness region class of the second image that has undergone bloom processing, where the second image is displayed before the first image; A generation module, configured to generate a bloom image of the first image based on the first image and the first intermediate image.

13. The device according to claim 12, wherein, The first image is rendered based on a first three-dimensional scene, and the second image is rendered based on a second three-dimensional scene. The fact that the first brightness region class is the same as the target brightness region class includes: The state information of the object models corresponding to the brightness regions in the first brightness region class in the first three-dimensional scene is the same as the state information of the object models corresponding to the brightness regions in the target brightness region class in the second three-dimensional scene, and the camera parameters of the first three-dimensional scene are the same as the camera parameters of the second three-dimensional scene.

14. The device according to claim 13, characterized in that, The apparatus further includes: A third acquisition module, configured to acquire status information of all object models corresponding to the first brightness region class in the first three-dimensional scene, and status information of all object models corresponding to the target brightness region class in the second three-dimensional scene; A fourth acquisition module, configured to acquire camera parameters of the first three-dimensional scene and camera parameters of the second three-dimensional scene after determining that the status information of all object models corresponding to the first brightness region class in the first three-dimensional scene is the same as the status information of all object models corresponding to the target brightness region class in the second three-dimensional scene; A determination module, configured to determine that the first brightness region class is the same as the target brightness region class after determining that the camera parameters of the first three-dimensional scene are the same as the camera parameters of the second three-dimensional scene.

15. The device according to claim 13 or 14, characterized in that The status information of the object model includes: pose information and surface material information of the object model, and the camera parameters include: pose parameters, window parameters, and field of view parameters of the camera.

16. The device according to claim 13 or 14, characterized in that, The first acquisition module is configured to: Traverse labels of all object models in the first three-dimensional scene, where the labels are used to indicate whether the object model is a background class object model; Acquire all the background class object models in the first three-dimensional scene, and the first brightness region class includes background class brightness regions corresponding to all the background class object models in the first image.

17. The device according to claim 16, characterized in that, The first image further includes a second brightness region, and the second brightness region class includes other brightness regions in the first image except the background class brightness regions. The apparatus further includes: A Gaussian blur processing module, configured to perform Gaussian blur processing on the second brightness region class to obtain a second intermediate image; The generation module is configured to: Perform image fusion processing on the first image, the first intermediate image, and the second intermediate image to obtain a glare image of the first image.

18. The device according to claim 12, characterized in that, The first image further includes a second brightness region class, and the second brightness region class includes one or more brightness regions of the first image. The apparatus further includes: A Gaussian blur processing module, configured to perform Gaussian blur processing on the second brightness region class to obtain a second intermediate image after determining that the second brightness region class of the first image is different from any brightness region class of the second image; The generation module is configured to: Perform image fusion processing on the first image, the first intermediate image, and the second intermediate image to obtain a glare image of the first image.

19. The device according to claim 17 or 18, characterized in that, The sizes of the brightness regions in the first brightness region class are all larger than the sizes of the brightness regions in the second brightness region class. The Gaussian blur processing module is configured to: Perform downsampling pixel sampling processing on the first image using a first reduction ratio to obtain a first reduced image; Perform Gaussian blur processing on the first reduced image to obtain the second intermediate image; Alternatively, the sizes of the brightness regions in the first brightness region class are all smaller than the sizes of the brightness regions in the second brightness region class. The Gaussian blur processing module is configured to: Perform downsampling pixel sampling processing on the first image using a second reduction ratio to obtain a second reduced image; Perform Gaussian blur processing on the second reduced image to obtain the second intermediate image; Wherein, the first reduction magnification is greater than the second reduction magnification.

20. The device according to claim 19, characterized in that, The first reduction ratio k1 satisfies: k1 = 2 n , where n is an integer and -3 ≤ n ≤ 0; the second reduction ratio k2 satisfies: k2 = 2 m , where m is an integer and m < -3.

21. The device according to any one of claims 12 to 14 and 18, characterized in that, The apparatus further includes: A filtering processing module, configured to perform brightness filtering processing on the first image before generating a bloom image of the first image based on the first image and the first intermediate image.

22. The device according to any one of claims 12 to 14 and 18, characterized in that The first image and the second image are two consecutive frames of images, and the second image is the previous frame image of the first image.

23. A storage medium, characterized in that, The storage medium stores a computer program, and the computer program instructs the processing component to execute the image bloom processing method according to any one of claims 1 to 11.

24. An image floodlight processing device, characterized in that, Including: A processor and a memory; The memory is configured to store a computer program, and the computer program includes program instructions; The processor is configured to call the computer program to implement the image bloom processing method according to any one of claims 1 to 11.

Images